Genetic Programming and Evolvable Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evolutionary Constructive Induction
IEEE Transactions on Knowledge and Data Engineering
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Breast cancer diagnosis using genetic programming generated feature
Pattern Recognition
A generic multi-dimensional feature extraction method using multiobjective genetic programming
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Dimensionality reduction using symbolic regression
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the International Conference on Advanced Visual Interfaces
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Selecting good views of high-dimensional data using class consistency
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Generation of neural networks using a genetic algorithm approach
International Journal of Bio-Inspired Computation
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Visual analytics is a human-machine collaboration to data modeling where extraction of the most informative features plays an important role. Although feature extraction is a multi-objective task, the traditional algorithms either only consider one objective or aggregate the objectives into one scalar criterion to optimize. In this paper, we propose a Pareto-based multi-objective approach to feature extraction for visual analytics applied to data classification problems. We identify classifiability, visual interpretability and semantic interpretability as the three equally important objectives for feature extraction in classification problems and define various measures to quantify these objectives. Our results on a number of benchmark datasets show consistent improvement compared to three standard dimensionality reduction techniques. We also argue that exploration of the multiple Pareto-optimal models provide more insight about the classification problem as opposed to a single optimal solution.