1. Define the problem
Spectral data from control and infected samples were collected over time for 15 days. The goal seems to be to utilize the data to classify whether each sample is a control or an infected sample To do this, we need to learn how the data changes over time, and design a model so that these changes reflect the differences between the control and infected samples We assume that the data is a simple time series, as we need to look closely at the structure of the data to design a model.
2.Data preprocessing
Bring all the data together and sort it into a structure of number of samples, time axis (1-15 days), and spectral bands (285). Each band value is normalized or standardized to clarify differences between spectra. This ensures that scale differences in the data do not affect model training.
3. Model design
The model is designed based on a Long Short-Term Memory (LSTM) network. LSTMs are well-suited to learning time-series data, and we use them to learn the pattern of spectral changes over 15 days for each sample. The size of the cells should be more than the variation or minimum periodicity. If you are looking at the overall behavior of the data and see changes at specific points in time, it may be better to utilize an attentional structure.
4. Evaluate the importance of the time scale
Instead of using all 15 days of data in the model training process, evaluate whether data up to a certain date is sufficient for training. For example, train the model using only data from days 1 through 5, or only data from days 1 through 10, and compare the performance. This allows you to see the minimum date range where model performance converges.
If you are able to generate a model based on LSTM, evaluate its importance, and find some variation in the timing of the changes, we recommend using a mixture of attitudes, otherwise, you may want to start with a modified LSTM or feature engineering.
