Abstract
Evaluation functions, used to measure the quality of features, have great influence on the feature selection algorithms in areas of data mining and knowledge discovery. However, the existing evaluation functions are often inadequately measured candidate features on cost-sensitive heterogeneous data. To address this problem, an entropy-based evaluation function is firstly proposed for measuring the uncertainty for heterogeneous data. To further evaluate the quality of candidate features, we propose a multi-criteria based evaluation function, which attempts to find candidate features with the minimal total costs and the same information as the whole feature set. On this basis, a cost-sensitive feature selection algorithm on heterogeneous data is developed. Compared with the existing feature selection algorithms, the experimental results show that the proposed algorithm is more efficient to find a subset of features without losing the classification performance.
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Qian, W., Shu, W., Yang, J., Wang, Y. (2015). Cost-Sensitive Feature Selection on Heterogeneous Data. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_31
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DOI: https://doi.org/10.1007/978-3-319-18032-8_31
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