An unsupervised feature selection algorithm based on ant colony optimization

Tabakhi, Sina; Moradi, Parham; Akhlaghian, Fardin

doi:10.1016/j.engappai.2014.03.007

Cited by 348 publications

(135 citation statements)

References 42 publications

Supporting

Mentioning

128

Contrasting

Unclassified

Order By: Relevance

“…In the case of S-shaped and V-shaped conversion functions, the number of each dimension feature data belonging to the optimal feature subset is counted, as shown in Figure 7. It can be seen from Figure 7 that the number of the 3rd, 13th, 22nd, 23rd, 24th, and 31st dimensions of the optimal feature subsets is the largest, but the final optimal feature subsets {1, 3,10,11,12,13,14,18,19,21,23,24,29,30,31} do not incorporate all of these higherfrequency features (that is, the optimal feature subset is not a simple combination of features with high frequency). The optimal feature subset does not necessarily include features with high frequency, since the feature subset selected by features of high frequency only may not have the best classification effect.…”

Section: V1mentioning

confidence: 99%

“…Therefore, the methods using this approach are typically fast but they need a threshold as the stopping criterion for feature selection. Several filter-based methods have been proposed in the literature including information gain, 10 gain ratio, 11 term variance, 12 Gini index, 13 Laplacian score, 14 Fisher score, 15 minimal-redundancy-maximal-relevance, 16 random subspace method, 17 relevance-redundancy feature selection, 18 unsupervised feature selection method based on ant colony optimization (UFSACO), 19 relevance-redundancy feature selection based on ant colony optimization (RRFSACO), 20 graph clustering with node centrality for feature selection method (GCNC), 21 and graph clustering based ant colony optimization feature selection method (GCACO). 22 Wrapper-based methods combine feature selection with the design of the classifier and evaluate the feature subsets on the basis of the accuracy of classification.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Effective feature selection determines the efficiency and accuracy of a learning process, which is essential in human activity recognition. In existing works, for simplification purposes, feature selection algorithms are mostly based on the assumption of feature independence. However, in some scenarios, the optimization method based on this independence hypothesis results in poor recognition performance. This article proposes a correlation-based binary particle swarm optimization method for feature selection in human activity recognition. In the proposed algorithm, the particle swarm optimization algorithm is no longer used as a black box. Meanwhile, correlation coefficients among the features are added to binary particle swarm optimization as a feature correlation factor to determine the position of particles, so that the feature with more information is more likely to be selected. The k-nearest neighbor classifier is then used as the fitness function in the particle swarm optimization to evaluate the performance of the feature subset, that is, feature combination with the highest k-nearest neighbor classifier recognition rate would be picked as the eigenvector. Experimental results show that the proposed method can work well with six classifiers, namely, J48, random forest, knearest neighbor, multilayer perceptron, naïve Bayesian, and support vector machine, and the new algorithm can improve the classification accuracy in the OPPORTUNITY Activity Recognition dataset.

show abstract

Section: V1mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Thus, the unsupervised feature selection problem becomes even more challenging than the supervised feature selection problem [5].…”

Section: Introductionmentioning

confidence: 99%

“…S. Tabakhi et al presented an unsupervised feature selection method based on ant colony optimization (UFSACO), the method seeks to find the optimal feature subset through several iterations without using any learning algorithms [5]. Pabitra Mitra et al described an unsupervised feature selection algorithm suitable for datasets, large in both dimension and size [6], the method is based on measuring similarity between features.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Selection Method Based on K-Nearest Neighbor Approach

Wang¹,

Zhang²,

Ma³

2017

Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

View full text Add to dashboard Cite

Abstract. In many data analysis tasks, one is often confronted with very high dimensional data. Feature selection is an effective method to solve the problem with high dimensional data. The aim of feature selection is to reduce the number of features used in classification or recognition. This reduction is expected to improve the performance of classification and clustering algorithms in terms of speed, accuracy and simplicity. This paper proposes a new unsupervised feature selection algorithm which is based on K-nearest neighbor approach. The proposed algorithm evaluates the whole features according to the each sample in the dataset by the K-nearest neighbor approach. After that, the overall assessment is given based on the assessment of features for each sample. We evaluate the performance of the proposed unsupervised feature selection algorithm using the well-known UCI machine learning datasets , and the results illustrates the proposed algorithm is comparable with the traditional feature selection algorithm.

show abstract

“…EAs mimic various social behaviors existing in nature to solve the optimization problems. Some popular EAs include genetic algorithm (GA) [1,2], particle swarm optimization (PSO) [3,4], artificial immune system (AIS) [5], differentiable evolution (DE) [6,7], ant colony optimization (ACO) [8], artificial bee colony (ABC) [9,10], and simulated annealing algorithm (SA) [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Biogeography‐Based Optimization Using Multitopology and Quantitative Orthogonal Learning

Wen

Chen

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.

show abstract

An unsupervised feature selection algorithm based on ant colony optimization

Cited by 348 publications

References 42 publications

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

A correlation-based binary particle swarm optimization method for feature selection in human activity recognition

A New Feature Selection Method Based on K-Nearest Neighbor Approach

Enhancing the Performance of Biogeography‐Based Optimization Using Multitopology and Quantitative Orthogonal Learning

Contact Info

Product

Resources

About