DATA CLEANINGTOOL: USAGEOFFUZZYROUGHSETTHEORY AS MACHINE LEARNINGPRE-PROCESSING | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 4, Volume 15, Issue 1, January 2015, Page 41-54 PDF (313.09 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2015.10909 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| B Hameed1; A Elfetouh2; M Abu_Elkheir3 | ||||
| 1Information System Department Information Technology Department Faculty of Computers and Information, Mansoura University-Egypt | ||||
| 2Information System Department Faculty of Computers and Information, Mansoura University-Egypt | ||||
| 3Information Technology Department Faculty of Computers and Information, Mansoura University-Egypt | ||||
| Abstract | ||||
| Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a crucial phase in the data mining process that involves techniques toresolve such issues. Feature selection is a popular data preprocessing procedure that is focused on omitting attributes from decision systems while still maintain the ability of those decision systems to distinguish different decision classes. A popular way to evaluate attribute subsets with respect to this criterion is based on the notion of dependency degree. In this paper, we conduct an experimental study using the generalized classical rough set framework for data-based attribute selection and reduction, based on the notion of fuzzy decision reducts to evaluate the viability of using Fuzzy rough subset feature. Experimental results shows that, general optimization can be achieved under average accuracy reduction, ±10.7 %, against high reduction rate over attributesranging from 36% to 97% and over instances from 1.7% to 44%. | ||||
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