ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, I ssue 8, August 2014 Copyright to IJIRCCE www.ijircce.com 5380 A Novel Approach on Ensemble Classifiers with Fast Rotation Forest Algorithm D.Gopika 1 , B.Azhagusundari 2 1 Research Scholar, Dr.Mahalingam Centre for Research and Development, N.G.M College, Pollachi, India. 2 Assistant Professor, Dr.Mahalingam Centre for Research and Development, N.G.M College, Pollachi, India. ABSTRACT: 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. KEYWORDS: Ensemble-methods, Classification, Data stream, Random Forest, Rotation Forest, Decision Trees, Principal component analysis I. INTRODUCTION Combining classifiers is a very popular research area known under different names in the literature such as committees of learners, mixture of experts, classifier ensembles, multiple classifier systems, and consensus theory [1]. The basic idea is to use more than one classifier, hoping that the overall accuracy will be better. Ensemble performances depend on two main properties: the individual accuracy and diversity [14]. Principal component analysis (PCA) is a technique that is useful for the compression and classification of data. The purpose is to reduce the dimensionality of a data set (sample) by finding a new set of variables, smaller than the original set of variables, that nonetheless retains most of the sample's information. By information we mean the variation present in the sample, given by the correlations between the original variables [2] [15]. The new variables, called principal components (PCs), are uncorrelated, and are ordered by the fraction of the total information each retains. Decision tree is a predictive model that uses a set of binary rules applied to calculate a target value and it can be used for classification (categorical variables) or regression (continuous variables) applications. Rules are developed using software available in many statistics packages. In decision tree different algorithms are used to determine the “best” split at a node. It is easy to interpret the decision rules and it is nonparametric so it is easy to incorporate a range of numeric or categorical data layers and there is no need to select unimodel data and classification is fast once rules are developed [3] [13]. Random Forest (RF) is a classification and regression method based on the aggregation of a large number of decision trees. Specifically, it is an ensemble of trees constructed from a training data set and internally validated to