A scalable saliency-based Feature selection method with instance level information
This addresses the need for instance-specific feature relevance in machine learning, offering a scalable solution that is incremental over existing saliency techniques.
The paper tackled the problem of classic feature selection methods not providing instance-level relevance information by developing a saliency-based feature selection method, which was shown to work successfully with neural networks and any gradient descent-trained architecture.
Classic feature selection techniques remove those features that are either irrelevant or redundant, achieving a subset of relevant features that help to provide a better knowledge extraction. This allows the creation of compact models that are easier to interpret. Most of these techniques work over the whole dataset, but they are unable to provide the user with successful information when only instance information is needed. In short, given any example, classic feature selection algorithms do not give any information about which the most relevant information is, regarding this sample. This work aims to overcome this handicap by developing a novel feature selection method, called Saliency-based Feature Selection (SFS), based in deep-learning saliency techniques. Our experimental results will prove that this algorithm can be successfully used not only in Neural Networks, but also under any given architecture trained by using Gradient Descent techniques.