Feature selection with Ant Colony Optimization and its applications for pattern recognition in space imagery

Neagoe, Victor-Emil; Neghina, Catalina

doi:10.1109/iccomm.2016.7528323

Cited by 12 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The special population-based structure and ecient parallel search manner enable EC methods to have very good global search ability. Some famous EC methods used in feature selection are: genetic algorithms [Ghareb et al 2016;Holland 1975], genetic programming [Kamath et al 2012;Koza 1990], ant colony optimization [Dorigo and Gambardella 1997;Neagoe and Neghina 2016], particle swarm optimization (PSO) [Bharti and Singh 2016;Kennedy and Eberhart 1995;Zhang et al 2017aZhang et al , 2015, dierential evolution [Al-Ani et al 2013;Storn and Price 1997], and the rey algorithm [Yang 2008;Zhang et al 2017b]. A survey of all kinds of work for solving feature selection problems using EC methods can be found in Ref.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

Xue

Zhang

2019

ACM Trans. Knowl. Discov. Data

297

View full text Add to dashboard Cite

Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to find a suitable EC method and corresponding suitable parameter values for a given large-scale feature selection problem if we want to solve it effectively and efficiently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for large-scale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classification accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems.

show abstract

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

Xue

Zhang

2019

ACM Trans. Knowl. Discov. Data

297

View full text Add to dashboard Cite

show abstract

“…Some of the most famous heuristic algorithms are Simulated Annealing (SA) which is designed based on the thermodynamic effects [4], Particle Swarm Optimization (PSO) which mimics the behavior of birds [5], Ant Colony Optimization (ACO) which simulates the foraging process of ants [6], and Genetic Algorithm (GA) which is inspired by Darwin's theory of evolution [7]. These algorithms are well suited to solve complex computational problems and show good performance in many fields such as function optimization [8], data clustering [9], pattern recognition [10], data mining [11], image processing [12], computer vision [13] and neural network training [14]. However, most of the existing algorithms have the disadvantage of premature convergence.…”

Section: Introductionmentioning

confidence: 99%

A Modified Gravitational Search Algorithm for Function Optimization

Zhu

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton's laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence. In this paper, the concept of repulsive force is introduced and the definition of exponential Kbest is used in a new version of GSA, which is called repulsive GSA with exponential Kbest (EKRGSA). In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force. In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established. Moreover, the exponential Kbest significantly decreases the computational time. The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms. The experimental results confirm the high efficiency of EKRGSA.

show abstract

Firefly Algorithm and Its Variants in Digital Image Processing: A Comprehensive Review

Dey

Chaki

Moraru

et al. 2019

Springer Tracts in Nature-Inspired Computing

View full text Add to dashboard Cite

Feature selection with Ant Colony Optimization and its applications for pattern recognition in space imagery

Cited by 12 publications

References 8 publications

Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

A Modified Gravitational Search Algorithm for Function Optimization

Firefly Algorithm and Its Variants in Digital Image Processing: A Comprehensive Review

Contact Info

Product

Resources

About