IncrementalClassificationECOC Predict
Libraries:
Statistics and Machine Learning Toolbox /
Incremental Learning /
Classification /
ECOC
Description
The IncrementalClassificationECOC Predict block classifies observations using a trained error-correcting output codes (ECOC) classification model returned as the output of an IncrementalClassificationECOC Fit block.
Import an initial ECOC classification model object into the block by specifying the name of a workspace variable that contains the object. The input port mdl receives a bus signal that represents an incremental learning model fit to streaming data. The input port x receives a chunk of predictor data (observations), and the output port label returns predicted class labels for the chunk. You can add optional output ports score and pbscore, where score returns predicted class scores (negated average binary losses), and pbscore returns positive-class scores for binary learners.
Examples
Perform Incremental Learning Using IncrementalClassificationECOC Fit and Predict Blocks
Perform incremental learning with the IncrementalClassificationECOC Fit block and predict labels with the IncrementalClassificationECOC Predict block.
- Since R2024a
- Open Live Script
Configure Simulink Template for Rate-Based Incremental Linear Classification
Configure the Simulink Rate-Based Incremental Learning template to perform incremental linear classification.
- Since R2024a
- Open Live Script
Configure Simulink Template for Conditionally Enabled Incremental Linear Classification
Configure the Simulink Enabled Execution Incremental Learning template to perform incremental linear classification.
- Since R2024a
- Open Live Script
Ports
Input
mdl — Incremental learning model
bus signal
Incremental learning model (incrementalClassificationECOC
) fit to streaming data, specified as a bus
signal (see Composite Signals
(Simulink)).
x — Chunk of predictor data
numeric matrix
Chunk of predictor data, specified as a numeric matrix. The orientation of the
variables and observations is specified by Predictor data observation
dimension. The default orientation is rows
, which
indicates that observations in the predictor data are oriented along the rows of
x.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Output
label — Chunk of predicted class labels
column vector
Chunk of predicted class labels, returned as a column vector. The label
label(i)
represents the class yielding the highest score for
the observation x(i
). For more details, see
the Label
argument of the predict
object function.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
| enumerated
score — Predicted class scores or posterior probabilities
matrix
Predicted class scores (negated average binary losses) or posterior probabilities,
returned as a matrix. To check the order of the classes, use the
ClassNames
property of the model specified by Select initial machine
learning model.
Dependencies
To enable this port, select the check box for Add output port for predicted class scores on the Main tab of the Block Parameters dialog box.
The block supports two decoding schemes that specify how the block aggregates the binary losses to compute the classification scores, and how the block determines the predicted class for each observation. For details, see Decoding scheme and Binary Loss and Decoding Scheme.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
pbscore — Positive-class scores of binary learners
matrix
Positive-class scores of binary learners, returned as a matrix. To check the class
assignment codes for the binary learners, use the CodingMatrix
property of the model specified by Select trained machine
learning model. For more details, see Coding Design
of a ClassificationECOC
object.
Dependencies
To enable this port, select the check box for Add output port for positive-class scores of binary learners on the Main tab of the Block Parameters dialog box.
Data Types: single
| double
| half
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| Boolean
| fixed point
Parameters
Main
Select initial machine learning model — Initial incremental classification ECOC model
ecocMdl
(default) | incrementalClassificationECOC
model object
Specify the name of a workspace variable that contains the configured
incrementalClassificationECOC
model object. The NumPredictors
property of the initial model must be a positive integer
scalar, and must be equal to the number of predictors in
x.
Programmatic Use
Block Parameter:
InitialLearner |
Type: workspace variable |
Values:
incrementalClassificationECOC model object |
Default:
"ecocMdl" |
Add output port for predicted class scores — Add second output port
off
(default) | on
Select the check box to include the output port score in the incrementalClassificationECOC Predict block.
Programmatic Use
Block Parameter:
ShowOutputScore |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Add output port for positive-class scores of binary learners — Add third output port
off
(default) | on
Select the check box to include the third output port pbscore in the incrementalClassificationECOC Predict block.
Programmatic Use
Block Parameter:
ShowOutputPBScore |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Binary learner loss function — Binary learner loss function
hinge
(default) | binodeviance
| exponential
| hamming
| linear
| logit
| quadratic
Specify the binary learner loss function as
binodeviance
, exponential
,
hamming
, hinge
,
linear
, logit
, or
quadratic
.
The recommended binary loss function depends on the score ranges returned by the binary learners. The following table lists some common cases:
Description | Recommended Function |
---|---|
All binary learners are linear classification models of logistic regression learners. | quadratic |
All binary learners are SVMs or linear classification models of SVM learners. | hinge |
You specify to predict class posterior probabilities by setting
when you train the ECOC
model. | quadratic |
For definitions of the loss functions, see Binary Loss and Decoding Scheme.
Programmatic Use
Block Parameter:
BinaryLoss |
Type: character vector |
Values:
"binodeviance" | "exponential" |
"hamming" | "hinge" |
"linear" | "logit" |
"quadratic" |
Default:
"hinge" |
Decoding scheme — Decoding scheme
lossweighted
(default) | lossbased
Specify the decoding scheme that aggregates the binary losses as
lossweighted
or
lossbased
.
The definition of the score values depends on the Decoding scheme value.
If you specify
lossweighted
, then the kth element in score is the sum of the binary losses divided by the number of binary learners for the kth class.If you specify
lossbased
, then the kth element in score is the sum of the binary losses divided by the total number of binary learners.
For more details, see Binary Loss and Decoding Scheme.
Programmatic Use
Block Parameter:
Decoding |
Type: character vector |
Values:
"lossweighted" | "lossbased" |
Default:
"lossweighted" |
Predictor data observation dimension — Observation dimension of predictor data
rows
(default) | columns
Specify the observation dimension of the predictor data. The default value is
rows
, which indicates that observations in the predictor data are
oriented along the rows of x.
Programmatic Use
Block Parameter:
ObservationsIn |
Type: character vector |
Values:
"rows" | "columns" |
Default:
"rows" |
Sample time (–1 for inherited) — Option to specify sample time
–1
(default) | scalar
Specify the discrete interval between sample time hits or specify another type of sample
time, such as continuous (0
) or inherited (–1
). For more
options, see Types of Sample Time (Simulink).
By default, the IncrementalClassificationECOC Predict block inherits sample time based on the context of the block within the model.
Programmatic Use
Block Parameter:
SystemSampleTime |
Type: string scalar or character vector |
Values: scalar |
Default:
"–1" |
Data Types
Fixed-Point Operational ParametersInteger rounding mode — Rounding mode for fixed-point operations
Floor
(default) | Ceiling
| Convergent
| Nearest
| Round
| Simplest
| Zero
Specify the rounding mode for fixed-point operations. For more information, see Rounding Modes (Fixed-Point Designer).
Block parameters always round to the nearest representable value. To control the rounding of a block parameter, enter an expression into the mask field using a MATLAB® rounding function.
Programmatic Use
Block Parameter:
RndMeth |
Type: character vector |
Values:
"Ceiling" | "Convergent" | "Floor" | "Nearest" | "Round" | "Simplest" |
"Zero" |
Default:
"Floor" |
Saturate on integer overflow — Method of overflow action
off
(default) | on
Specify whether overflows saturate or wrap.
Action | Rationale | Impact on Overflows | Example |
---|---|---|---|
Select this check box
( | Your model has possible overflow, and you want explicit saturation protection in the generated code. | Overflows saturate to either the minimum or maximum value that the data type can represent. | The maximum value that the |
Clear this check box
( | You want to optimize the efficiency of your generated code. You want to avoid overspecifying how a block handles out-of-range signals. For more information, see Troubleshoot Signal Range Errors (Simulink). | Overflows wrap to the appropriate value that the data type can represent. | The maximum value that the |
Programmatic Use
Block Parameter:
SaturateOnIntegerOverflow |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Lock output data type setting against changes by the fixed-point tools — Prevention of fixed-point tools from overriding data type
off
(default) | on
Select this parameter to prevent the fixed-point tools from overriding the data type you specify for the block. For more information, see Use Lock Output Data Type Setting (Fixed-Point Designer).
Programmatic Use
Block Parameter:
LockScale |
Type: character vector |
Values:
"off" | "on" |
Default:
"off" |
Label data type — Data type of label output
Inherit: Inherit via back propagation
| double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| Enum: <class name>
| <data type expression>
Specify the data type for the label output. The type can be
inherited, specified as an enumerated data type, or
expressed as a data type object such as Simulink.NumericType
.
The supported data types depend on the labels used in the model specified by Select initial machine learning model.
If the model uses numeric or logical labels, the supported data types are
Inherit: Inherit via back propagation
(default),double
,single
,half
,int8
,uint8
,int16
,uint16
,int32
,uint32
,int64
,uint64
,boolean
, fixed point, and a data type object.If the model uses nonnumeric labels, the supported data types are
Enum: <class name>
and a data type object.
When you select an inherited option, the software behaves as follows:
Inherit: Inherit via back propagation
(default for numeric and logical labels) — Simulink® automatically determines the Label data type of the block during data type propagation (see Data Type Propagation (Simulink)). In this case, the block uses the data type of a downstream block or signal object.Inherit: auto
(default for nonnumeric labels) — The block uses an autodefined enumerated data type variable. For example, suppose the workspace variable name specified by Select initial machine learning model ismyMdl
, and the class labels areclass 1
andclass 2
. Then, the corresponding label values aremyMdl_enumLabels.class_1
andmyMdl_enumLabels.class_2
. The block converts the class labels to valid MATLAB identifiers by using thematlab.lang.makeValidName
function.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
LabelDataTypeStr |
Type: character vector |
Values: "Inherit: Inherit via back
propagation" | "double" |
"single" | "half" |
"int8" | "uint8" |
"int16" | "uint16" |
"int32" | "uint32" |
"int64" | "uint64" |
"boolean" | "fixdt(1,16,0)" |
"fixdt(1,16,2^0,0)" | "Enum: <class
name>" | "<data type expression>" |
Default: "Inherit: Inherit via
back propagation" |
Label data type Minimum — Minimum value of label output for range checking
[]
(default) | scalar
Specify the lower value of the label output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Label data type Minimum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.
Dependencies
You can specify this parameter only if the model specified by Select initial machine learning model uses numeric labels.
Programmatic Use
Block Parameter:
LabelOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Label data type Maximum — Maximum value of label output for range checking
[]
(default) | scalar
Specify the upper value of the label output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Label data type Maximum parameter does not saturate or clip the actual label output signal. To do so, use the Saturation (Simulink) block instead.
Dependencies
You can specify this parameter only if the model specified by Select initial machine learning model uses numeric labels.
Programmatic Use
Block Parameter:
LabelOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Score data type — Data type of score output
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the score output. The type can be
inherited, specified directly, or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that
inherits a data
type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
ScoreDataTypeStr |
Type: character vector |
Values: "Inherit: auto"
| "double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"
| "<data type expression>" |
Default: "Inherit:
auto" |
Score data type Minimum — Minimum value of score output for range checking
[]
(default) | scalar
Specify the lower value of the score output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Score data type Minimum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
ScoreOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Score data type Maximum — Maximum value of score output for range checking
[]
(default) | scalar
Specify the upper value of the score output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Score data type Maximum parameter does not saturate or clip the actual score output. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter:
ScoreOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Positive-class score data type — Positive-class score data type
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the pbscore output. This data type also determines the data type for the classification scores of binary learners. The type can be inherited, specified directly, or expressed as a data type object such as Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule that inherits a data type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter: PBScoreDataTypeStr |
Type: character vector |
Values: "Inherit: auto" |
"double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"
| "<data type expression>" |
Default: "Inherit: auto" |
Positive-class score data type Minimum — Minimum value of pbscore
output for range checking
[]
(default) | scalar
Specify the lower value of the pbscore output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Positive-class score data type Minimum parameter does not saturate or clip the actual pbscore signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter: PBScoreOutMin |
Type: character vector |
Values: '[]' | scalar |
Default: '[]' |
Positive-class score data type Maximum — Maximum value of pbscore
output for range checking
[]
(default) | scalar
Specify the upper value of the pbscore output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Positive-class score data type Maximum parameter does not saturate or clip the actual pbscore signal. To do so, use the Saturation (Simulink) block instead.
Programmatic Use
Block Parameter: PBScoreOutMax |
Type: character vector |
Values: '[]' | scalar |
Default: '[]' |
Binary learner score data type — Binary learner score data type
Inherit: auto
(default) | double
| single
| half
| int8
| uint8
| int16
| uint16
| int32
| uint32
| int64
| uint64
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type for the binary learner scores that are internal to the
IncrementalClassificationECOC Predict
block. The type can be inherited, specified directly,
or expressed as a data type object such as
Simulink.NumericType
.
When you select Inherit: auto
, the block uses a rule
that inherits a data
type.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Programmatic Use
Block Parameter:
BinaryScoreDataTypeStr |
Type: character vector |
Values: "Inherit: auto"
| "double" | "single" |
"half" | "int8" |
"uint8" | "int16" |
"uint16" | "int32" |
"uint32" | "int64" |
"uint64" | "boolean" |
"fixdt(1,16,0)" | "fixdt(1,16,2^0,0)" |
"<data type expression>" |
Default: "Inherit:
auto" |
Binary learner score data type Minimum — Minimum value of binary learner score for range checking
[]
(default) | scalar
Specify the lower value of the binary learner score output range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Programmatic Use
Block Parameter:
BinaryScoreOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Binary learner score data type Maximum — Maximum value of binary learner score for range checking
[]
(default) | scalar
Specify the upper value of the binary learner score output range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Programmatic Use
Block Parameter:
BinaryScoreOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Binary learner kernel data type — Binary learner kernel computation data type
double
(default) | single
| half
| int8
| uint8
| int16
| uint16
| int32
| int64
| uint64
| uint32
| boolean
| fixdt(1,16,0)
| fixdt(1,16,2^0,0)
| <data type expression>
Specify the data type of a parameter for kernel computation of binary learners.
The type can be specified directly or expressed as a data type object such as
Simulink.NumericType
.
For more information about data types, see Control Data Types of Signals (Simulink).
Click the Show data type assistant button to display the Data Type Assistant, which helps you set the data type attributes. For more information, see Specify Data Types Using Data Type Assistant (Simulink).
Dependencies
You can specify this parameter only if the model specified by Select trained machine learning model uses SVM learners. If the model uses linear learners, then specify Inner product data type instead.
The IncrementalClassificationECOC Predict block supports only linear kernels. The
Binary learner kernel data type parameter specifies the data
type for the output of the linear kernel function , where x is the predictor data for an
observation and s is a support vector. You specify the kernel
function type by using the KernelFunction
name-value argument of the templateSVM
function. You must pass
the output of templateSVM
as the value for the Learners
name-value
argument of the fitcecoc
.
Programmatic Use
Block Parameter:
BinaryKernelDataTypeStr |
Type: character vector |
Values: "double" |
"single" | "half" |
"int8" | "uint8" |
"int16" | "uint16" |
"int32" | "uint32" |
"uint64" | "int64" |
"boolean" | "fixdt(1,16,0)" |
"fixdt(1,16,2^0,0)" | "<data type
expression>" |
Default:
"double" |
Binary learner kernel data type Minimum — Minimum kernel computation value for range checking
[]
(default) | scalar
Specify the lower value of the kernel computation internal variable range that Simulink checks.
Simulink uses the minimum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as software-in-the-loop (SIL) mode or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Binary learner kernel data type Minimum parameter does not saturate or clip the actual kernel computation value signal.
Programmatic Use
Block Parameter:
BinaryKernelOutMin |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Binary learner kernel data type Maximum — Maximum kernel computation value for range checking
[]
(default) | scalar
Specify the upper value of the kernel computation internal variable range that Simulink checks.
Simulink uses the maximum value to perform:
Parameter range checking for some blocks (see Specify Minimum and Maximum Values for Block Parameters (Simulink)).
Simulation range checking (see Specify Signal Ranges (Simulink) and Enable Simulation Range Checking (Simulink)).
Optimization of the code that you generate from the model. This optimization can remove algorithmic code and affect the results of some simulation modes, such as SIL or external mode. For more information, see Optimize using the specified minimum and maximum values (Embedded Coder).
Note
The Binary learner kernel data type Maximum parameter does not saturate or clip the actual kernel computation value signal.
Programmatic Use
Block Parameter:
BinaryKernelOutMax |
Type: character vector |
Values: "[]" |
scalar |
Default: "[]" |
Block Characteristics
Data Types |
|
Direct Feedthrough |
|
Multidimensional Signals |
|
Variable-Size Signals |
|
Zero-Crossing Detection |
|
More About
Binary Loss and Decoding Scheme
The binary loss is a function of the class and classification score that determines how well a binary learner classifies an observation into the class. The decoding scheme of an ECOC model specifies how the software aggregates the binary losses and determines the predicted class for each observation.
Assume the following:
mkj is element (k,j) of the coding design matrix M—that is, the code corresponding to class k of binary learner j. M is a K-by-B matrix, where K is the number of classes, and B is the number of binary learners.
sj is the score of binary learner j for an observation.
g is the binary loss function.
is the predicted class for the observation.
The IncrementalClassificationECOC Predict block supports two decoding schemes:
Loss-based decoding [2] (Decoding scheme is
lossbased
) — The predicted class of an observation corresponds to the class that produces the minimum average of the binary losses over all binary learners.Loss-weighted decoding [3] (Decoding scheme is
lossweighted
) — The predicted class of an observation corresponds to the class that produces the minimum average of the binary losses over the binary learners for the corresponding class.The denominator corresponds to the number of binary learners for class k. As suggested in [1], loss-weighted decoding improves classification accuracy by keeping loss values for all classes in the same dynamic range.
The block returns the negated value of the objective function of
argmin
as the second output port (score
) for each
observation and class.
This table summarizes the supported binary loss functions, where yj is a class label for a particular binary learner (in the set {–1,1,0}), sj is the score for observation j, and g(yj,sj) is the binary loss function. You can specify a binary loss function by using Binary learner loss function.
Value | Description | Score Domain | g(yj,sj) |
---|---|---|---|
"binodeviance" | Binomial deviance | (–∞,∞) | log[1 + exp(–2yjsj)]/[2log(2)] |
"exponential" | Exponential | (–∞,∞) | exp(–yjsj)/2 |
"hamming" | Hamming | [0,1] or (–∞,∞) | [1 – sign(yjsj)]/2 |
"hinge" | Hinge | (–∞,∞) | max(0,1 – yjsj)/2 |
"linear" | Linear | (–∞,∞) | (1 – yjsj)/2 |
"logit" | Logistic | (–∞,∞) | log[1 + exp(–yjsj)]/[2log(2)] |
"quadratic" | Quadratic | [0,1] | [1 – yj(2sj – 1)]2/2 |
The software normalizes binary losses so that the loss is 0.5 when yj = 0, and aggregates using the average of the binary learners [1].
References
[1] Allwein, E., R. Schapire, and Y. Singer. “Reducing multiclass to binary: A unifying approach for margin classifiers.” Journal of Machine Learning Research. Vol. 1, 2000, pp. 113–141.
[2] Escalera, S., O. Pujol, and P. Radeva. “Separability of ternary codes for sparse designs of error-correcting output codes.” Pattern Recog. Lett. Vol. 30, Issue 3, 2009, pp. 285–297.
[3] Escalera, S., O. Pujol, and P. Radeva. “On the decoding process in ternary error-correcting output codes.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 32, Issue 7, 2010, pp. 120–134.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.
Version History
Introduced in R2024a
See Also
Blocks
Objects
Functions
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