Two Step Ant Colony System to Solve the Feature Selection Problem

In classification of multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if only consider image's spectral feature or texture feature alone. In this paper ,we presents a new approach by applying Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to multispectral remote sense image's classification. Using the combination of the spectral and texture features obtained by ACO in classification always get a better accuracy.

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“…Other parts of our ACO (i.e. decision rule and pheromone update method) are same to ACS [16] [17] [18] [19].…”

Section: Applying Aco To Find a Suitable Subset Featuresmentioning

confidence: 99%

“…However, how to find the optimal combination of spectral and textural features to form a multi-feature vector is a new problem. In this work, we propose to adopt Ant Colony Optimization (ACO) [16], [17], [18] to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral Remote Sensing Image

Wen

Yin

Guo

2008

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium

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“…They are based on the reduct concept. A new approach to use ACO in the feature selection in which the search process developed by ants is split into two stages was presented in [18]. In this approach some parameters (number of ants, quantity of cycles, etc.)…”

Section: Introductionmentioning

confidence: 99%

Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem

Bello¹,

Gomez²,

Nowé³

et al. 2007

Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007)

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In this paper we propose a new model of ParticleSwarm Optimization called Two-Step PSO. The basic idea is to split the heuristic search performed by particles into two stages. We have studied the performance of this new algorithm for the Feature Selection problem by using the reduct concept of the Rough Set Theory. Experimental results obtained show that the Two-step approach improves over the PSO model in calculating reducts, with the same computational cost.

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“…A new approach in Ant Colony Optimization for solving the feature selection problem was introduced in [3] . The two-step ACO algorithm is also based on the idea of splitting the process of finding reducts into two stages.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…Some of them have exhibited good results, mainly attained by using ACO-or PSO-based approaches, such as [11,1,2,24,25,26]. In [3] and [4], a new approach to feature selection based on the ACO and PSO methodologies is presented. The chief thought is the split of the search process accomplished by the agents (ants or particles) into two stages, such that an agent is commanded in the first stage to find a partial solution to the problem, which in turn is afterwards used as an initial state during the upcoming phase.…”

Section: Introductionmentioning

confidence: 99%

Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem

Bello

Gómez

Caballero

et al.

Studies in Computational Intelligence

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Summary. The feature selection problem has been usually addressed through heuristic approaches given its significant computational complexity. In this context, evolutionary techniques have drawn the researchers' attention owing to their appealing optimization capabilities. In this chapter, promising results achieved by the authors in solving the feature selection problem through a joint effort between rough set theory and evolutionary computation techniques are reviewed. In particular, two new heuristic search algorithms are introduced, i.e. Dynamic Mesh Optimization and another approach which splits the search process carried out by swarm intelligence methods.

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Two Step Ant Colony System to Solve the Feature Selection Problem

Cited by 13 publications

References 13 publications

Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral Remote Sensing Image

Ant Colony Optimization Algorithm for Feature Selection and Classification of Multispectral Remote Sensing Image

Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem

Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem

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