Feature selection by combining feature importance and redundancy estimatesNajdova, Elena (Avtor) Džeroski, Sašo (Mentor) feature selectionmachine learningmaximum relevance minimum redundancyclassificationpredictive modelFeature selection is a crucial task in the process of building predictive models in machine learning. Understanding which data is relevant for our problem and what feature selection method is most compatible with our data is of vital importance to achieve good results. The goal of this thesis was to develop and implement software for feature selection in the machine learning task of classification, based on the maximum relevance minimum redundancy (MRMR) approach. We aimed to create a software which can be used with different measures of relevance and redundancy. This software is then used to compare the performance of the developed approach (choosing k features with the MRMR approach) to the performance of the classical filtering approach, where the top k features with highest relevance are selected (and their redundancy is ignored). We analyze the results and draw conclusions from them. With appropriate relevance and redundancy measures, the MRMR approach can perform much better than the filtering approach.20232023-08-21 17:23:43Diplomsko delo/naloga148393VisID: 36692COBISS_ID: 158150403sl