CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification^|^sup1;

Abstract

The associative classification method integrates association rule mining and classification. Constructing an efficient classifier with a small set of high quality rules is a highly important but indeed a challenging task. The lazy learning associative classification method successfully removes the need for a classifier but suffers from high computation costs. This paper proposes a Compact Highest Subset Confidence-Based Associative Classification scheme that generates compact subsets based on information gain and classifies the new samples without constructing classifiers. Experimental results show that the proposed system out performs both the traditional and the existing lazy learning associative classification methods.

Lazy Learning Associative Classification with WkNN and DWkNN Algorithm

“…The accuracy comparison is shown in Table 2, where dataset name is tabulated in column 1; 2nd column is the traditional method Lazy Associative Classification LAC [2], 3rd, 4th and 5th columns are existing lazy learning methods namely Lazy Associative Classification using Information gain (LACI) [9], Compact Highest Subset Confidence-based Associative Classification (CHiSC-AC) [15] and Attribute ranking based lazy learning AC [12] It can be seen in the comparison result that the proposed LLAC-WkNN system is 10.17% better than LAC, 8.23% better than LACI, 3.43% better than CHiSC and 0.40% better than ARBLazyAC respectively. Proposed LLAC-DWkNN is 13.97% better than LAC, 11.97% better than LACI, 7.00% better than CHiSC and 3.91% better than ARBLazyAC respectively.…”

“…Decision Tree is used in [14] for the early prediction of the Diabetes. The compact subset generation method is used in ChiSC-AC method [15]. Multiple algorithms and methods are presented by multiple authors, but still there is the possibility to improve the accuracy of the classifier.…”

A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

“…Other techniques for mining CARs have been suggested in recent years. They include GARC [5], ECR-CARM [6], CBC [7], CAR-Miner [8], CHISC-AC [9] and developed d2O [10]. The methods of classification based on CARs were demonstrated to be more accurate than the classic methods e.g.…”