Module Combination Based On Decision Tree In Min-max Modular Network With Application To Patent Classification

Large-scale pattern recognition problems always restrict real applications of manymachine learning algorithms. These problems are usually very common, such as patentclassification. Even for efficient algorithms such as Support Vector Machine, large-scale problem are still too tough to learn. It is quite feasible to employ af?uent com-puting resources, and apply parallel computing environment ro solve these large-scaleproblems.Min-Max Modular Support Vector Machine(M3-SVM) is a"divide and conquer"based algorithm which can effectively solve large-scale problems. To reduce the timecomplexity in module combination step in M3-SVM, we proposed a Decision treeClassifier Selection(DCS) algorithms. DCS is an evolution of Symmetric ClassifierSelection(SCS). The results of experiments show that DCS can do classification asgood as SCS, and it can reduce the time complexity in prediction step.We also proposed a data selection method when training a decision tree. It canhighly reduce both the complexity of decision tree building and the decision tree size.Because of the smaller size, parallel computing can be applied better in DCS thanSCS. DCS can also save the memory space.We have done many experiments, such as two-spiral problem and patent classifi-cation, to prove our conclusion.