A Novel Approach on Ensemble Classifiers with Fast Rotation Forest Algorithm
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Ensemble approaches in classification are a very popular research area in recent years. An ensemble consists of a set of individual classifiers such as neural networks or decision trees whose predictions are combined for classifying new instances. A method is used here for generating classifier ensembles based on feature extraction. In the base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. It is a technique that is useful for the extraction and classification of data. The purpose is to reduce the dimensionality of a data set. Then the Decision tree is used to classify the data set. Rotation Forest and Extended Space Forest algorithms are used to calculate the accuracy. A novel approach Fast Rotation Forest is introduced to enrich the accuracy rate. The idea of the fast rotation approach is to encourage simultaneously individual accuracy and specificity within the ensemble. By comparing Random forest and Extended Space Forest, Fast Rotation Forest yields high accuracy. Using WEKA, fast rotation forest is examined on a random selection of 10 medical data sets from the UCI repository and compared it with bagging, Extended Space Forest, and random forest. The results were favorable to fast rotation forest.