| Autor: |
Sailaja, N. Venkata1 sailaja_nv@vnrvjiet.in, Sree, L. Padma2 padmasree_l@vnrvjiet.in, Mangathayaru, N.3 mangathayaru_n@vnrvjiet.in |
| Předmět: |
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| Zdroj: |
Journal of Information & Knowledge Management. Jun2018, Vol. 17 Issue 2, pN.PAG-N.PAG. 19p. |
| Abstrakt: |
With the advent of computers and the information age, statistical and analytical problems have grown in terms of both size and complexity. Challenges in core domains of data storage, organisation and searching have evolved to the new research field called data mining. Text classification using various machine learning (ML) mechanisms encounters the difficulty of the high dimensionality of attributes vector. Therefore, a feature selection technique is very much required to discard irrelevant as well as noisy attributes from the feature set vector so that the ML algorithms can work efficiently. In this paper, rough set theory (RST)-based attribute selection methodology is applied to achieve text classification goal. A hybrid method based on RST is proposed for text documents classification. Further, the proposed method’s performance is evaluated on standard datasets. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Library, Information Science & Technology Abstracts |
| Externí odkaz: |
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