Complexity of rule sets in mining incomplete data using characteristic sets and generalized maximal consistent blocks. (18th September 2020)

• Record Type: Journal Article
• Authors: Clark, Patrick G; Gao, Cheng; Grzymala-Busse, Jerzy W; Mroczek, Teresa; Niemiec, Rafal
• Is Part Of: Logic journal of the IGPL. Volume 29:Number 2(2021)
• Page Start: 124
• Page End: 137
• Publication Date: 2020-09-18
• Subjects: Incomplete data -- characteristic sets -- maximal consistent blocks -- MLEM2 rule induction algorithm -- probabilistic approximations; Logic, Symbolic and mathematical -- Periodicals; 511.3
• DOI: 10.1093/jigpal/jzaa041
• Languages: English
• ISSNs: 1367-0751

Abstract

Abstract: In this paper, missing attribute values in incomplete data sets have three possible interpretations: lost values, attribute-concept values and 'do not care' conditions. For rule induction, we use characteristic sets and generalized maximal consistent blocks. Therefore, we apply six different approaches for data mining. As follows from our previous experiments, where we used an error rate evaluated by ten-fold cross validation as the main criterion of quality, no approach is universally the best. Thus, we decided to compare our six approaches using complexity of rule sets induced from incomplete data sets. We show that the smallest rule sets are induced from incomplete data sets with attribute-concept values, while the most complicated rule sets are induced from data sets with lost values. The choice between interpretations of missing attribute values is more important than the choice between characteristic sets and generalized maximal consistent blocks.