Learn More
Clustering ensembles have emerged as a powerful method for improving both the robustness as well as the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial, or statistical perspectives. This study extends previous(More)
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. We offer a probabilistic model of(More)
—Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to(More)
This paper summarizes work on an approach that combines feature selection and data classiication using Genetic Algorithms. First, it describes our use of Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classiication by searching for an optimal feature weight-ing, essentially warping the feature space to coalesce individuals(More)
This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize(More)