This paper addresses the well-known classification task of data mining, where the goal is to discover rules predicting the class of examples (records of a given data set). In the context of data mining, small disjuncts are rules covering a small number of examples. Hence, these rules are usually error-prone, which contributes to a decrease in predictive accuracy. At first glance, this is not a serious problem, since the impact on predictive accuracy should be small. However, although each small disjunct covers few examples, the set of all small disjuncts can cover a large number of examples. This paper presents evidence that this is the case in several data sets. This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic algorithm approach. In essence, examples belonging to large disjuncts are classified by rules produced by a decision-tree algorithm (C4.5), while examples belonging to small disjuncts are classified by a genetic algorithm specifically designed for discovering small-disjunct rules. We present results comparing the predictive accuracy of this hybrid system with the prediction accuracy of three versions of C4.5 alone in eight public domain data sets. Overall, the results show that our hybrid system achieves better predictive accuracy than all three versions of C4.5 alone.
In addition, a hybrid decision tree/-genetic algorithm is used to discover small disjunct rules in REF .
A genetic algorithm for discovering small disjunct rules in data mining
Applied Soft Computing
Computer Science