About a Distance Measure and Application for Finding Reduct in Incomplete Decision Tables
Nguyen Anh Tuan1, Nguyen Long Giang2
1Nguyen Anh Tuan*, Vinh Phuc College Phuc Yen, Vinh Phuc Province, Vietnam.
2Nguyen Long Giang, Institute of Information Technology, Vietnam Academy of Science and Technology; Hanoi, Vietnam.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6294-6298 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1436109119/2019©BEIESP | DOI: 10.35940/ijeat.A1436.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Tolerance rough set model is an effective tool to reduce attributes in incomplete decision tables. Over 40 years, several attribute reduction methods have been proposed to improve the efficiency of execution time and the number of attributes of the reduct. However, they are classical filter algorithms, in which the classification accuracy of decision tables is computed after obtaining the reducts. Therefore, the obtained reducts of these algorithms are not optimal in terms of reduct cardinality and classification accuracy. In this paper, we propose a filter-wrapper algorithm to find a reduct in incomplete decision tables. We then use this measure to determine the importance of the property and select the attribute based on the calculated importance (filter phase). In the next step, we find the reduct with the highest classification accuracy by iterating over elements of the set containing the sequence of attributes selected in the first step (wrapper phase). To verify the effectiveness of the method, we conduct experiments on 6 famous UCI data sets. Experimental results show that the proposed method increase classification accuracy as well as reduce the cardinality of reduct compared to Algorithm 1 .
Keywords: Attribute reduction, Distance, Incomplete decision table, Reduct, Tolerance rough set.