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I am using a CNN based model to do sequence classification. Since training an entire dataset is very expensive, and I have a large set of features needed to try, its impossible for me to select features by full trainings.

Usually a good sanity check before training, is try to let model overfit a small set of training samples, to make sure the model is at least capable of remember a small sample size.

Borrowed from this idea, my question is, can I train a small subset of training data, and use its loss curve as a metric, to select best features? Each training is to test how quick would the training loss converges given a subset of selected features.

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  • $\begingroup$ Regarding the details of our problem, I didn't understand few points. Your input is a sequence. Are you using RNN or CNN? Besides, how do you extract the features from the sequence? If the input to your algorithm is a fixed set of feature, having a sequence as their source is not an important issue. $\endgroup$
    – DaL
    Commented Nov 14, 2017 at 7:44
  • $\begingroup$ I am actually using CNN to do stock market prediction, the input is a sequence of market behavior, features are extracted using varies methods, price differences, trading volume, indicators etc.. $\endgroup$
    – Xer
    Commented Nov 14, 2017 at 7:51
  • $\begingroup$ And how do you get from the sequence to the features? Do you use the convolutions for feature extraction or another layer? $\endgroup$
    – DaL
    Commented Nov 14, 2017 at 7:54
  • $\begingroup$ No, its all hand crafted features, hopping it can ease the training. $\endgroup$
    – Xer
    Commented Nov 14, 2017 at 7:56

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Congratulations! You have suggested independently the Wrapper method for feature selection. Yes, you can use this method. However, consider that the wrapper method is slow since you have to train a model for each iteration. More than that, feature selection is a NP-complete problem so don't expect the optimal subset.

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