The Strength of Weak Learnability
Machine Learning
Neural Computation
Artificial Intelligence Review - Special issue on lazy learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Global and local neural network ensembles
Pattern Recognition Letters
A selective sampling approach to active feature selection
Artificial Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Adaptive mixtures of local experts
Neural Computation
SIAM Journal on Matrix Analysis and Applications
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence Review
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Efficient Algorithm for Localized Support Vector Machine
IEEE Transactions on Knowledge and Data Engineering
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Semi-Supervised Learning
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Microarray data can be used to detect diseases and predict responses to therapies through classification models. However, the high dimensionality and low sample size of such data result in many computational problems such as reduced prediction accuracy and slow classification speed. In this paper, we propose a novel family of nonnegative least-squares classifiers for high-dimensional microarray gene expression and comparative genomic hybridization data. Our approaches are based on combining the advantages of using local learning, transductive learning, and ensemble learning, for better prediction performance. To study the performances of our methods, we performed computational experiments on 17 well-known data sets with diverse characteristics. We have also performed statistical comparisons with many classification techniques including the well-performing SVM approach and two related but recent methods proposed in literature. Experimental results show that our approaches are faster and achieve generally a better prediction performance over compared methods.