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Local Feature Selection For Multiple Instance Learning With Applications., Aliasghar Shahrjooihaghighi
Local Feature Selection For Multiple Instance Learning With Applications., Aliasghar Shahrjooihaghighi
Electronic Theses and Dissertations
Feature selection is a data processing approach that has been successfully and effectively used in developing machine learning algorithms for various applications. It has been proven to effectively reduce the dimensionality of the data and increase the accuracy and interpretability of machine learning algorithms. Conventional feature selection algorithms assume that there is an optimal global subset of features for the whole sample space. Thus, only one global subset of relevant features is learned. An alternative approach is based on the concept of Local Feature Selection (LFS), where each training sample can have its own subset of relevant features. Multiple Instance …