Improving Support Vector Machine Using a Stochastic Local Search for Classification in DataMining

Nekkaa, Messaouda; Boughaci, Dalila

doi:10.1007/978-3-642-34481-7_21

Cited by 5 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Between HSA-SLS 2 , MA + SVM, SLS + SVM and GA + SVM Table 6 gives the average (Mean), the best (Max), the worst (Min) values of the classification accuracy, and the standard deviation (Sd) obtained by the HSA-SLS 2 , MA + SVM optimi zed [35], SLS + SVM [33,35] and GA + SVM [12]. As shown in Table 6, HSA-SLS 2 succeeds in finding the best results for almost the checked datasets compared to MA + SVM optimi zed , SLS + SVM and GA + SVM methods.…”

Section: Comparisonmentioning

confidence: 99%

“…The SLS is a local search meta-heuristic which has been already studied for several optimization problem such as the satisfiability problem and the optimal winner determination problem in combinatorial auctions [6,33]. The SLS starts with each individual x i belongs to Ω that selected by a probabilistic selection strategy from the HM for each iteration NI of the proposed HSA-SLS method.…”

Section: Refinement With Stochastic Local Searchmentioning

confidence: 99%

“…Several methods have been studied for feature selection. Among them, we cite the stochastic local search (SLS) [33], the genetic algorithms [5,30,34,39,40] and the memetic algorithm proposed recently by Nekkaa and Boughaci [35].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

Nekkaa

Boughaci

2015

Neural Process Lett

Self Cite

View full text Add to dashboard Cite

Feature selection is a challenging task that has been the subject of a large amount of research, especially in relation to classification tasks. It permits to eliminate the redundant attributes and enhance the classification accuracy by keeping only the relevant attributes. In this paper, we propose a hybrid search method based on both harmony search algorithm (HSA) and stochastic local search (SLS) for feature selection in data classification. A novel probabilistic selection strategy is used in HSA-SLS to select the appropriate solutions to undergo stochastic local refinement, keeping a good compromise between exploration and exploitation. In addition, the HSA-SLS is combined with a support vector machine (SVM) classifier with optimized parameters. The proposed HSA-SLS method tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experimental results show good performance in favor of our proposed method.

show abstract

Section: Comparisonmentioning

confidence: 99%

Section: Refinement With Stochastic Local Searchmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

Nekkaa

Boughaci

2015

Neural Process Lett

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several methods have been developed for feature selection. Among them, the stepwise forward selection, the stepwise backward elimination techniques, 8 the best-first search, 9 the stochastic local search (SLS), 10 the harmony search, 11 the genetic algorithms, 1,[12][13][14] the Memetic algorithm, 15 the particle swarm optimization, 16 and the hyper-heuristics. 17,18…”

Section: Feature Selectionmentioning

confidence: 99%

A synergy Thompson sampling hyper‐heuristic for the feature selection problem

Lassouaoui

Boughaci

Benhamou³

2020

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Summary To classify high‐dimensional data, feature selection plays a key role to eliminate irrelevant attributes and enhance the classification accuracy and efficiency. Since feature selection is an NP‐Hard problem, many heuristics and metaheuristics have been used to tackle in practice this problem. In this article, we propose a novel approach that consists in a probabilistic selection hyper‐heuristic called the synergy Thompson sampling hyper‐heuristic. The Thompson sampling selection strategy is a probabilistic reinforcement learning mechanism to assess the behavior of the low‐level heuristics, and to predict which one will be more efficient at each point during the search process. The proposed hyper‐heuristic is combined with a 1 nearest neighbor classifier from the Weka framework. It aims to find the best subset of features that maximizes the classification accuracy rate. Experimental results show a good performance in favor of the proposed method when comparing with other existing approaches.

show abstract

“…In information fusion algorithm, the features extracted from multi-sensor data may be redundant and contain false correlations, leading to high computational complexity, and low classification accuracy [11]. Thus, reducing redundancy of features is necessary and important in fusion algorithms.…”

Section: Introductionmentioning

confidence: 99%

Gaussian kernel-based Fuzzy Rough Set for information fusion of imperfect images

Shi

Chen

Qin

et al. 2014

2014 12th International Conference on Signal Processing (ICSP)

View full text Add to dashboard Cite

Imperfection of remote sensing data greatly affects the performance of information fusion algorithm. To solve this problem, a Gaussian kernel-based Fuzzy Rough Set fusion algorithm is proposed, since Fuzzy Rough Set theory is an effect tool to model uncertainties of data. For feature reduction a novel index is proposed to evaluate the significance of features, considering both the relevance between features and decisions and the redundancy of features. Thus the most informative features are selected for classification. Experiments with standard test data and real remote sensing data show that the classifier can achieve a high accuracy using feature subset selected by the proposed method than using the full feature set.Index Terms-imperfect information, feature selection, fuzzy rough set, information fusion

show abstract

Improving Support Vector Machine Using a Stochastic Local Search for Classification in DataMining

Cited by 5 publications

References 5 publications

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

A synergy Thompson sampling hyper‐heuristic for the feature selection problem

Gaussian kernel-based Fuzzy Rough Set for information fusion of imperfect images

Contact Info

Product

Resources

About