Building Emerging Pattern (EP) Random forest for recognition

The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree classifiers. However, the classification performances of these tree classifiers are different. The random forest classifier ignores the difference by simply assigning them equal weights in voting for the final classification decision. Also, the random forest classifier only casts votes from individual tree classifiers without considering their compositions which would be more accurate. In this paper, we propose to tackle the two points by discovering weighted decision rules from the tree classifiers' output sets on training data. By treating the outputs of the tree classifiers on each data as a digital itemset, we want to find discriminative patterns (either containing the output of a single tree classifier or a set of tree classifiers) from the itemsets of training data. We employ an efficient data mining algorithm, the Emerging Pattern (EP) Mining, to search such discriminative patterns and weight them according to their discriminative powers. A set of decision rules are built from these discovered patterns and the final outputs of the Random Forest are made using these decision rules. We call the proposed classifier Emerging Pattern (EP) Random Forest. Experimental results on action categorization problems confirm that the proposed method really improve the performance of the traditional Random Forest classifier.