Online Streaming Feature Selection via Conditional Independence

Online feature selection is a challenging topic in data mining. It aims to reduce the dimensionality of streaming features by removing irrelevant and redundant features in real time. Existing works, such as <i>Alpha-investing</i> and Online Streaming Feature Selection (<i>OSFS</i>), have been proposed to serve this purpose, but they have drawbacks, including low prediction accuracy and high running time if the streaming features exhibit characteristics such as low redundancy and high relevance. In this paper, we propose a novel algorithm about online streaming feature selection, named <i>ConInd</i> that uses a three-layer filtering strategy to process streaming features with the aim of overcoming such drawbacks. Through three-layer filtering, i.e., null-conditional independence, single-conditional independence, and multi-conditional independence, we can obtain an approximate <i>Markov blanket</i> with high accuracy and low running time. To validate the efficiency, we implemented the proposed algorithm and tested its performance on a prevalent dataset, i.e., <i>NIPS 2003</i> and <i>Causality Workbench</i>. Through extensive experimental results, we demonstrated that <i>ConInd</i> offers significant performance improvements in prediction accuracy and running time compared to <i>Alpha-investing</i> and <i>OSFS</i>. <i>ConInd</i> offers 5.62% higher average prediction accuracy than <i>Alpha-investing,</i> with a 53.56% lower average running time compared to that for <i>OSFS</i> when the dataset is lowly redundant and highly relevant. In addition, the ratio of the average number of features for <i>ConInd</i> is 242% less than that for <i>Alpha-investing</i>.

Abstract licence: CC BY

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