GRACER: Improving the Accuracy of RACER Classifier Using A Greedy Approach

Rule-based classifiers play a prominent role in the field of data mining as they are used frequently in tackling challenges from medical to industrial applications. RACER (Rule Aggregating ClassifiER), a recently-introduced algorithm, is one of the rule-based classifiers that has had increased the accuracy of classification compared to some other well-known classifiers. RACER considers each instance in the training data as an initial rule. To form an applicable rule-set, however, RACER tries to merge the initial rules and replaces the merged rule with the two initial rules whenever it has a better fitness value than the initial rules. In this paper, we have changed the rule-merging phase of the RACER classifier using a greedy approach and have proposed a new classifier based on this modification called, GRACER (Greedy RACER). In order to approve the GRACER capability, six datasets from the UCI machine learning …