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• W2955241807 abstract "Document dimensionality is a major concern and worrying factor when high dimensionality documents are used for classification. Reducing the dimensionality can have both positive and negative effects. If dimensionality reduction is not appropriate then the classification performed using the reduced dimensionality documents may not give good classification results. Our previous research was focused on addressing dimensionality reduction using novel similarity function, but it did not address text classification. This paper addresses the classification task performed by applying the proposed similarity function. Experiment results prove the classifier performance with dimensionality reduction is better to the performance without dimensionality reduction." @default.
• W2955241807 created "2019-07-12" @default.
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• W2955241807 date "2019-06-06" @default.
• W2955241807 modified "2023-09-27" @default.
• W2955241807 title "High dimensional document classification using novel similarity function" @default.
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• W2955241807 doi "https://doi.org/10.1145/3330431.3330462" @default.
• W2955241807 hasPublicationYear "2019" @default.
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