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- W2604863809 abstract "In this paper, a feature selection method is presented for the multiclass data sets. This method is the hybridization of k-means clustering using cosine similarity as a distance measure and information Gain. In the method unsupervised Cosine Similarity is used for grouping of features i.e. K-means clustering is used to make a cluster of features and then information gain is employed to select a most relevant feature from each cluster. The dataset with the selected feature is tested for classification accuracy with cross - validation approach. Three classifiers namely Naive Bayes (NB), K-Nearest Neighbor and Classification and Regression trees (CART) has been used as the base classifiers for getting classification accuracy. Obtained results are compared with filter-based feature selection technique (Information Gain)." @default.
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- W2604863809 date "2016-01-01" @default.
- W2604863809 modified "2023-09-28" @default.
- W2604863809 title "A cluster-filter feature selection approach" @default.
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- W2604863809 doi "https://doi.org/10.1109/ictbig.2016.7892637" @default.
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