Generalized agreement statistics over fixed group of experts
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier
Expert Systems with Applications: An International Journal
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Anomaly detection via coupled gaussian kernels
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
Expert Systems with Applications: An International Journal
On evaluating stream learning algorithms
Machine Learning
A second order cone programming approach for semi-supervised learning
Pattern Recognition
Expert Systems with Applications: An International Journal
Customer behavior analysis using rough set approach
Journal of Theoretical and Applied Electronic Commerce Research
Engineering Applications of Artificial Intelligence
Combining block-based and online methods in learning ensembles from concept drifting data streams
Information Sciences: an International Journal
A method for evaluation of learning components
Automated Software Engineering
Information Sciences: an International Journal
Quantifying the reliability of fault classifiers
Information Sciences: an International Journal
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
Machine learning for science and society
Machine Learning
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The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation.Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.