Extending Naïve Bayes Classifiers Using Long Itemsets

Itemsets provide local descriptions of the data. This work proposes to use itemsets as basic means for classification purposes too. To enable this, the concept of class support sup of an itemset is introduced, i.e., how many times an itemset occurs when a specific class c i is present. Class supports of frequent itemsets are computed in the training phase. Upon arrival of a new case to be classified, some of the generated itemsets are selected and their class supports sup i are used to compute the probability that the case belongs to class c i . The result is the class c i with highest such probability. We show that selecting and combining many and long itemsets providing new evidence (interesting) is an effective strategy for computing the class probabilities. The proposed classification technique is called Large Bayes as it happens to reduce to Naïve Bayes classifier when all itemsets selected are of size one only. Experimental results on a large number of benchmark data sets show th..