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- W3214728101 abstract "In machine learning, feature selection is a very important step to reduce the dimensionality of data by removing irrelevant features, redundant data to improve the learning accuracy. As the dimensionality of data has increased, feature selection has become a challenging task. Various approaches have been proposed for feature selection. In this study, we have analyzed the effectiveness of three widely used feature selection methods namely Chi square; information gain and latent semantic analysis (LSA) to classify the software bugs. The performance of four classifiers K nearest neighbor, Random Forest, naïve bayes and support vector machine are evaluated for the above feature selection methods in terms of accuracy, precision and recall." @default.
- W3214728101 created "2021-11-22" @default.
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- W3214728101 date "2021-09-03" @default.
- W3214728101 modified "2023-10-16" @default.
- W3214728101 title "Bug Report Classification by Selecting Relevant Features Using Chi Square, Information Gain and Latent Semantic Analysis" @default.
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- W3214728101 doi "https://doi.org/10.1109/icrito51393.2021.9596496" @default.
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