Feature Selection Based on Divergence Functions: A Comparative Classiffication Study
نویسندگان
چکیده
Due to the extensive use of high-dimensional data and its application in a wide range scientifc felds research, dimensionality reduction has become major part preprocessing step machine learning. Feature selection is one procedure for reducing dimensionality. In this process, instead using whole set features, subset selected be used learning model. (FS) methods are divided into three main categories: flters, wrappers, embedded approaches. Filter only depend on characteristics data, do not rely model at hand. Divergence functions as measures evaluating differences between probability distribution can flter feature selection. paper, performances few divergence such Jensen-Shannon (JS) Exponential (EXP) compared with those some most-known Information Gain (IG) Chi-Squared (CHI). This comparison was made through accuracy rate F1-score classifcation models after implementing these methods.
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ژورنال
عنوان ژورنال: Statistics, Optimization and Information Computing
سال: 2021
ISSN: ['2310-5070', '2311-004X']
DOI: https://doi.org/10.19139/soic-2310-5070-1092