A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes

An improved multiple classifier combination scheme for pattern classification

Combining multiple classifiers are considered as a new direction in the pattern recognition to improve classification performance. The main problem of multiple classifier combination is that there is no standard guideline for constructing an accurate and diverse classifier ensemble. This is due to the difficulty in identifying the number of homogeneous classifiers and how to combine the classifier outputs. The most commonly used ensemble method is the random strategy while the majority voting technique is used as the combiner. However, the random strategy cannot determine the number of classifiers and the majority voting technique does not consider the strength of each classifier, thus resulting in low classification accuracy. In this study, an improved multiple classifier combination scheme is proposed. The ant system (AS) algorithm is used to partition feature set in developing feature subsets which represent the number of classifiers. A compactness measure is introduced as a parameter in constructing an accurate and diverse classifier ensemble. A weighted voting technique is used to combine the classifier outputs by considering the strength of the classifiers prior to voting. Experiments were performed using four base classifiers, which are Nearest Mean Classifier (NMC), Naive Bayes Classifier (NBC), k-Nearest Neighbour (k-NN) and Linear Discriminant Analysis (LDA) on benchmark datasets, to test the credibility of the proposed multiple classifier combination scheme. The average classification accuracy of the homogeneous NMC, NBC, k-NN and LDA ensembles are 97.91%, 98.06%, 98.09% and 98.12% respectively. The accuracies are higher than those obtained through the use of other approaches in developing multiple classifier combination. The proposed multiple classifier combination scheme will help to develop other multiple classifier combination for pattern recognition and classification

Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms

This paper proposes a novel approach to select the individual classifiers to take part in a Multiple-Classifier System. Individual classifier selection is a key step in the development of multi-classifiers. Several works have shown the benefits of fusing complementary classifiers. Nevertheless, the selection of the base classifiers to be used is still an open question, and different approaches have been proposed in the literature. This work is based on the selection of the appropriate single classifiers by means of an evolutionary algorithm. Different base classifiers, which have been chosen from different classifier families, are used as candidates in order to obtain variability in the classifications given. Experimental results carried out with 20 databases from the UCI Repository show how adequate the proposed approach is; Stacked Generalization multi-classifier has been selected to perform the experimental comparisons.The work described in this paper was partially conducted within the Basque Government Research Team grant and the University of the Basque Country UPV/EHU and under grant UFI11/45 (BAILab)

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes