Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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Ensemble methods such as bagging and boosting have been successfully applied to classification problems. Two important issues associated with an ensemble approach are: how to generate models to construct an ensemble, and how to combine them for classification. In this paper, we focus on the problem of model generation for heterogeneous data classification. If we could partition heterogeneous data into a number of homogeneous partitions, we will likely generate reliable and accurate classification models over the homogeneous partitions. We examine different ways of forming homogeneous subsets and propose a novel method that allows a data point to be assigned multiple times in order to generate homogeneous partitions for ensemble learning. We present the details of the new algorithm and empirical studies over the UCI benchmark datasets and datasets of image classification, and show that the proposed approach is effective for heterogeneous data classification.