An improvement direction for filter selection techniques using information theory measures and quadratic optimization
This work addresses feature selection for machine learning practitioners, but it appears incremental as it builds on existing filter methods with optimization.
The paper tackled the problem of redundant features in filter selection techniques, which reduces generalization performance, by proposing a mathematical optimization method that reduces inter-feature redundancy and maximizes relevance to the target variable, resulting in improved classification models.
Filter selection techniques are known for their simplicity and efficiency. However this kind of methods doesn't take into consideration the features inter-redundancy. Consequently the un-removed redundant features remain in the final classification model, giving lower generalization performance. In this paper we propose to use a mathematical optimization method that reduces inter-features redundancy and maximize relevance between each feature and the target variable.