Feature selection using forest optimization algorithm based on contribution degree

Ma, Tinghuai; Jia, Dongdong; Zhou, Hong‐Hao; Xue, Yu; Cao, Jie

doi:10.3233/ida-173636

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…According to the filter and wrapper categories, the unsupervised learning and supervised learning methods are adopted for feature selection. Forest Optimization Algorithm (FOA) and its improvments [Ma, Jia, Zhou et al (2018)] are supervised methods. Also, Particle Swarm Optimization (PSO) [Tran, Xue and Zhang (2014b)] method is supervised mthods.…”

Section: Related Workmentioning

confidence: 99%

“…Forest Optimization Algorithm (FOA) is an classic supervised learning, which was proposed by Ghaemi et al [Ghaemi and Feizi-Derakhshi (2014)]. And we propsed the feature contribution to promote the effecient based on Forest Optimization Algorithm [Ma, Jia, Zhou et al (2018)]. FOA as a evolutionary algorithms, which is suitable for optimization task.…”

Section: Foamentioning

confidence: 99%

“…At the same time, our algorithm is compared with other feature selection methods. It includes feature selection using forest optimization algorithm based on contribution degree(FSFOACD) [Ma, Jia, Zhou et al (2018)], feature selection based on ant colony optimization unsupervised probability algorithm (UPFS) [Dadaneh, Markid and Zakerolhosseini (2016)], feature selection based on supervised rough sets (ANFS) [Luo, Nie, Chang et al (2018)] and the feature selection of a novel initialization and update mechanism of particle swarm optimization (PSO(4-2)) [Tran, Xue and Zhang (2014b)].…”

Section: Global Seedingmentioning

confidence: 99%

“…According to the existing cognition, random search strategies coming up with approximately best solution are still our best choices for feature selection problem [Zhu, Xu, Chen et al (2016)]. Our previous work on feature selection research obtained a method called FSFOACD [Ma, Jia, Zhou et al (2018); Rong, Ma, Cao et al (2019)]. That method used the contribution degree strategy to improve the efficiency of the forest optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In order to verifying the effectiveness of our algorithm, 10 widely-used datasets from UCI are selected and three classifiers respectively from support vector machine (SVM) [Aburomman and Reaz (2016)], decision tree (DT) [Shirani, Habibi, Besalatpour et al (2015)] and k-nearest neighbor (KNN) [Benmahamed, Teguar and Boubakeur (2018)]. There are 4 feature selection methods are selected for comparison, including UPFS [Dadaneh, Markid and Zakerolhosseini (2016)], ANFS [Luo, Nie, Chang et al (2018)], PSO(4-2) [Tran, Xue and Zhang (2014b)] and FSFOACD [Ma, Jia, Zhou et al (2018)]. The experiments show that most of the results are superior to previous feature selection algorithms we selected.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Ma¹,

Zhou²,

Jia³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

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Feature selection has been widely used in data mining and machine learning. Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly. In this article, a feature selection algorithm with local search strategy based on the forest optimization algorithm, namely FSLSFOA, is proposed. The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest. Next, the fitness function is improved, which not only considers the classification accuracy, but also considers the size of the feature subset. To avoid falling into local optimum, a novel global seeding method is attempted, which selects trees on the bottom of candidate set and gives the algorithm more diversities. Finally, FSLSFOA is compared with four feature selection methods to verify its effectiveness. Most of the results are superior to these comparative methods.

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Section: Related Workmentioning

confidence: 99%

Section: Foamentioning

confidence: 99%

Section: Global Seedingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Ma¹,

Zhou²,

Jia³

et al. 2019

Computer Modeling in Engineering &Amp; Sciences

Self Cite

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Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm

Osmani

Mohasefi

Gharehchopogh

2020

The Computer Journal

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Artificial bee colony (ABC) optimization and imperialist competitive algorithm (ICA) are two famous metaheuristic methods. In ABC, exploration is good because each bee moves toward random neighbors in the first and second phases. In ABC, exploitation is poor because it does not try to examine a promising region of search space carefully to see if it contains a good local minimum. In this study, ICA is considered to improve ABC exploitation, and two novel swarm-based hybrid methods called ABC–ICA and ABC–ICA1 are proposed, which combine the characteristics of ABC and ICA. The proposed methods improve the evaluations results in both continuous and discrete environments compared to the baseline methods. The second method improves the first optimization method as well. Feature selection can be considered to be an optimization problem because selecting the appropriate feature subset is very important and the action of appropriate feature selection has a great influence on the efficiency of classifier algorithms in supervised methods. Therefore, to focus on feature selection is a key issue and is very important. In this study, different discrete versions of the proposed methods have been introduced that can be used in feature selection and feature scoring problems, which have been successful in evaluations. In this study, a problem called cold start is introduced, and a solution is presented that has a great impact on the efficiency of the proposed methods in feature scoring problem. A total of 16 UCI data sets and 2 Amazon data sets have been used for the evaluation of the proposed methods in feature selection problem. The parameters that have been compared are classification accuracy and the number of features required for classification. Also, the proposed methods can be used to create a proper sentiment dictionary. Evaluation results confirm the better performance of the proposed methods in most experiments.

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Information granularity-based incremental feature selection for partially labeled hybrid data

Shu

Yan

Chen

et al. 2022

IDA

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Feature selection can reduce the dimensionality of data effectively. Most of the existing feature selection approaches using rough sets focus on the static single type data. However, in many real-world applications, data sets are the hybrid data including symbolic, numerical and missing features. Meanwhile, an object set in the hybrid data often changes dynamically with time. For the hybrid data, since acquiring all the decision labels of them is expensive and time-consuming, only small portion of the decision labels for the hybrid data is obtained. Therefore, in this paper, incremental feature selection algorithms based on information granularity are developed for dynamic partially labeled hybrid data with the variation of an object set. At first, the information granularity is given to measure the feature significance for partially labeled hybrid data. Then, incremental mechanisms of information granularity are proposed with the variation of an object set. On this basis, incremental feature selection algorithms with the variation of a single object and group of objects are proposed, respectively. Finally, extensive experimental results on different UCI data sets demonstrate that compared with the non-incremental feature selection algorithms, incremental feature selection algorithms can select a subset of features in shorter time without losing the classification accuracy, especially when the group of objects changes dynamically, the group incremental feature selection algorithm is more efficient.

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Feature selection using forest optimization algorithm based on contribution degree

Cited by 5 publications

References 38 publications

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Sentiment Classification Using Two Effective Optimization Methods Derived From The Artificial Bee Colony Optimization And Imperialist Competitive Algorithm

Information granularity-based incremental feature selection for partially labeled hybrid data

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