Inferring decision trees using the minimum description length principle
Information and Computation
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Modern Information Retrieval
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Issues in Classifier Evaluation using Optimal Cost Curves
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Decision Rules by Randomized Iterative Local Search
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from Inconsistent and Noisy Data: The AQ18 Approach
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Toward Bayesian Classifiers with Accurate Probabilities
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Construct robust rule sets for classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge evaluation: Other evaluations: minimum description length
Handbook of data mining and knowledge discovery
An introduction to variable and feature selection
The Journal of Machine Learning Research
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficiently handling feature redundancy in high-dimensional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule quality for multiple-rule classifier: Empirical expertise and theoretical methodology
Intelligent Data Analysis
Journal of Artificial Intelligence Research
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Knowledge actionability: satisfying technical and business interestingness
International Journal of Business Intelligence and Data Mining
Proceedings of the 17th international conference on World Wide Web
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Evolutionary Optimization of Trading Strategies
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Towards Business Interestingness in Actionable Knowledge Discovery
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
Dependence modeling rule mining using multi-objective genetic algorithms
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Interactive selection of Web services under multiple objectives
Information Technology and Management
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
Agent-based evolutionary optimisation of trading strategies
International Journal of Intelligent Information and Database Systems
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Evolutionary model trees for handling continuous classes in machine learning
Information Sciences: an International Journal
Quality-driven resource-adaptive data stream mining?
ACM SIGKDD Explorations Newsletter
Evolutionary multi objective optimization for rule mining: a review
Artificial Intelligence Review
A novel non-dominated sorting algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Predicting software maintenance effort through evolutionary-based decision trees
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Self-disclosure decision making based on intimacy and privacy
Information Sciences: an International Journal
Strategic pseudonym change in agent-based e-commerce
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Automated buyer profiling control based on human privacy attitudes
Electronic Commerce Research and Applications
Information Sciences: an International Journal
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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This paper addresses the problem of how to evaluate the quality of a model built from the data in a multi-objective optimization scenario, where two or more quality criteria must be simultaneously optimized. A typical example is a scenario where one wants to maximize both the accuracy and the simplicity of a classification model or a candidate attribute subset in attribute selection. One reviews three very different approaches to cope with this problem, namely: (a) transforming the original multi-objective problem into a single-objective problem by using a weighted formula; (b) the lexicographical approach, where the objectives are ranked in order of priority; and (c) the Pareto approach, which consists of finding as many non-dominated solutions as possible and returning the set of non-dominated solutions to the user. One also presents a critical review of the case for and against each of these approaches. The general conclusions are that the weighted formula approach -- which is by far the most used in the data mining literature -- is to a large extent an ad-hoc approach for multi-objective optimization, whereas the lexicographic and the Pareto approach are more principled approaches, and therefore deserve more attention from the data mining community.