Robust regression and outlier detection
Robust regression and outlier detection
Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms
Computational Statistics & Data Analysis
Practical genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians
The Journal of Machine Learning Research
Scale-invariant clustering with minimum volume ellipsoids
Computers and Operations Research
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Image Segmentation Using Hidden Markov Gauss Mixture Models
IEEE Transactions on Image Processing
Trial pruning based on genetic algorithm for single-trial EEG classification
Computers and Electrical Engineering
Robust learning of mixture models and its application on trial pruning for EEG signal analysis
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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A robust mixture model-based clustering algorithm using genetic techniques is proposed in this paper. In many engineering and application domains, noisy samples and outliers often exist in data collections, causing negative effects on performance of data mining methods if they are not made aware of these elements. Classical probabilistic mixture-based clustering is one known to be very sensitive to such situation. To improve its performance, we combine Genetic Algorithm (GA) with the expectation-maximization (EM) procedure of the classical model. When trimmed likelihood is used as fitness function of GA, high representative samples are selected and potential outliers are pruned off effectively during the learning process. Experiments on both synthetic and real data for different applications show that our approach outperforms the classical mixture model, by producing more accurate and reliable results.