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136
Feature Subset Selection in Unsupervised Learning via Multiobjective Optimization
"... Abstract: In this paper, the problem of unsupervised feature selection and its formulation as a multiobjective optimization problem are investigated. Two existing multiobjective methods from the literature are revisited and used as the basis for an algorithmic framework, encompassing both wrapper an ..."
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Abstract: In this paper, the problem of unsupervised feature selection and its formulation as a multiobjective optimization problem are investigated. Two existing multiobjective methods from the literature are revisited and used as the basis for an algorithmic framework, encompassing both wrapper
Crossover Operators for Multiobjective k-Subset Selection
"... Genetic algorithms are often applied to combinatorial opti-mization problems, the most popular one probably being the traveling salesperson problem. In contrast to permutations used for TSP, the selection of a subset from a larger set has so far gained surprisingly little interest. One intriguing ex ..."
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Genetic algorithms are often applied to combinatorial opti-mization problems, the most popular one probably being the traveling salesperson problem. In contrast to permutations used for TSP, the selection of a subset from a larger set has so far gained surprisingly little interest. One intriguing
Feature selection in clustering problems
- In Advances in Neural Information Processing Systems 16
, 2004
"... A novel approach to combining clustering and feature selection is presented. It implements a wrapper strategy for feature selection, in the sense that the features are directly selected by optimizing the discriminative power of the used partitioning algorithm. On the technical side, we present an ef ..."
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Cited by 39 (2 self)
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A novel approach to combining clustering and feature selection is presented. It implements a wrapper strategy for feature selection, in the sense that the features are directly selected by optimizing the discriminative power of the used partitioning algorithm. On the technical side, we present
A Multiobjective Genetic Algorithm for Attribute Selection
"... Abstract: The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach. Th ..."
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Abstract: The problem of feature selection in data mining is an important real-world problem that involves multiple objectives to be simultaneously optimized. In order to tackle this problem this work proposes a multiobjective genetic algorithm for feature selection based on the wrapper approach
Feature Selection using Multi-objective Genetic Algorithm: A Hybrid Approach
"... Abstract. Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search ..."
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search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the feature subset under consideration. Hence, Multi-Objective Genetic Algorithms (MOGAs) are a
A Multiobjective Genetic Algorithm for Feature Selection in Data Mining
"... Abstract-The rapid advance of computer based highthroughput technique have provided unparalleled opportunities for humans to expand capabilities in production, services, communications, and research. Meanwhile, immense quantities of high-dimensional data are accumulated challenging state-of-the-art ..."
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Optimization to carry out the search for (quasi) optimal subsets of features considering possible conflicting importance criteria. This work presents an application of Multi-objective Genetic Algorithms to the Feature Selection problem, combining different criteria measuring the importance of the subsets
An Evolutionary Multi-Objective Local Selection Algorithm for Customer Targeting
- in: Proc. of Congress on Evolutionary Computation (CEC-01), accepted
"... In an increasingly competitive marketplace, one of the most interesting and challenging problems is how to identify and profile customers who are most likely to be interested in new products or services. At the same time, minimizing the number of variables used in the prediction task is important wi ..."
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Cited by 2 (1 self)
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with large databases. In this paper we consider a novel application of evolutionary multiobjective algorithms for customer targeting. Evolutionary algorithms are considered effective in solving multiobjective problems because of their inherent parallelism. We use ELSA, an evolutionary local selection
Feature Subset Selection using Ant Colony Optimization
- International Journal of Computational Intelligence
, 2006
"... Abstract—Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has b ..."
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Cited by 9 (2 self)
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Abstract—Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has
A Methodology for Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit String Recognition
- International Journal of Pattern Recognition and Artificial Intelligence
, 2003
"... In this paper a methodology for feature selection for the handwritten digit string recognition is proposed. Its novelty lies in the use of a multi-objective genetic algorithm where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate tness ..."
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Cited by 34 (9 self)
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In this paper a methodology for feature selection for the handwritten digit string recognition is proposed. Its novelty lies in the use of a multi-objective genetic algorithm where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate
Ant Colony Optimization for Feature Subset Selection
- Proceedings of World Academy of Science, Engineering and Technology
, 2005
"... Abstract—The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the import ..."
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Cited by 7 (0 self)
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to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained using the classification of speech segments are very promising. Keywords—Ant Colony Optimization
Results 1 - 10
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136
