Relevance Feature Search for Text Mining - Kamble, Rekha;More, Shivaprasad
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Relevance Feature Discovery is an innovative model that classifies terms into distinct categories and effectively updates term weights and distribution in patterns, hence boosting text mining performance.The terms that appear more frequently in relevant papers are regarded as positive specific terms. The terms that appear more frequently in irrelevant papers are classified as negative specific terms. The goal of Relevance Feature Discovery is to extract high-quality features that accurately represent the user's demands. This system outperforms term and pattern-based techniques.

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Produktbeschreibung
Relevance Feature Discovery is an innovative model that classifies terms into distinct categories and effectively updates term weights and distribution in patterns, hence boosting text mining performance.The terms that appear more frequently in relevant papers are regarded as positive specific terms. The terms that appear more frequently in irrelevant papers are classified as negative specific terms. The goal of Relevance Feature Discovery is to extract high-quality features that accurately represent the user's demands. This system outperforms term and pattern-based techniques.
Autorenporträt
Prof. Rekha Kamble completed Master of Technology in Computer Science & Engineering from DKTE Society's Textile & Engineering Institute, India in 2018. She works as Assistant Professor in Department of Computer Science & Engineering (AIML). Her research interests include data mining, text mining, blockchain technology and cloud computing.