Kernel Feature Selection to Improve Generalization Performance of Boosting Classifiers
نویسندگان
چکیده
In this paper, kernel feature selection is proposed to improve generalization performance of boosting classifiers. Kernel feature Selection attains the feature selection and model selection at the same time using a simple selection algorithm. The algorithm automatically selects a subset of kernel features for each classifier and combines them according to the LogitBoost algorithm. The system employs kernel logistic regression for the base-learner, and a kernel feature is selected at each stage of boosting to improve the generalization error. The proposed method was applied to the MIT CBCL pedestrian image database, and kernel features were extracted from each pixel of the images as a local feature. The experimental results showed good generalization error with local feature selection, while more improvement was achieved with the kernel feature selection.
منابع مشابه
Boosting k-nearest neighbor classifier by means of input space projection
The k-nearest neighbors classifier is one of the most widely used methods of classification due to several interesting features, such as good generalization and easy implementation. Although simple, it is usually able to match, and even beat, more sophisticated and complex methods. However, no successful method has been reported so far to apply boosting to k-NN. As boosting methods have proved ...
متن کاملA Learning Algorithm of Boosting Kernel Discriminant Analysis for Pattern Recognition
In this paper, we present a new method to enhance classification performance of a multiple classifier system by combining a boosting technique called AdaBoost.M2 and Kernel Discriminant Analysis (KDA). To reduce the dependency between classifier outputs and to speed up the learning, each classifier is trained in a different feature space, which is obtained by applying KDA to a small set of hard...
متن کاملSUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS
This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...
متن کاملFeature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets
Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...
متن کاملSVM and SVM Ensembles in Breast Cancer Prediction
Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary ...
متن کامل