Abstract
While most feature selection algorithms focus on finding relevant features, few take the redundancy issue into account. We propose a nonlinear redundancy measure which uses genetic programming to find the redundancy quotient of a feature with respect to a subset of features. The proposed measure is unsupervised and works with unlabeled data. We introduce a forward selection algorithm which can be used along with the proposed measure to perform feature selection over the output of a feature ranking algorithm. The effectiveness of the proposed method is assessed by applying it to the output of the Chi-square (χ 2) feature ranker on a classification task. The results show significant improvements in the performance of decision tree and SVM classifiers.
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Neshatian, K., Zhang, M. (2009). Unsupervised Elimination of Redundant Features Using Genetic Programming. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_44
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DOI: https://doi.org/10.1007/978-3-642-10439-8_44
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