An ensemble of decision cluster crotches for classification of high dimensional data

This paper presents a Crotch Ensemble classification model for high dimensional data. A Crotch Ensemble is obtained from a decision cluster tree built by calling a clustering algorithm recursively. A crotch is an inner node of the tree together with its direct children. If the children of a crotch have more than one dominant class, the crotch is defined as a crotch predictor. Each crotch predictor constructs a classifier by itself. A Crotch Ensemble consists of a set of crotch predictors. When classifying a new object, a subset of crotch predictors is selected according to the distances between the object and the crotch predictors. A classification is made on the object as the class predicted by the crotch predictors with the maximum accumulative weights. The experimental results on both synthetic and real data have shown that the Crotch Ensemble model can get better classification results on high dimensional data than other classification methods.

► This paper presents a new cluster-based classification method, i.e. Crotch Ensemble to explore the area of model selection from a decision cluster tree. ► This paper also presents a rigorous analysis of the underlined principles of Crotch Ensemble. ► We have compared the Crotch Ensemble with other six classification methods.