Ant Colony Optimization Toward Feature Selection

Kabir, Monirul; Shahjahan, Md; Murase, Kazuyuki

doi:10.5772/51707

Cited by 12 publications

(8 citation statements)

References 56 publications

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“…In this regard, ACOFS heuristic information measurement rules rely and modify the standard pheromone update. The reason of the novelty and clarity of ACOFS opposite other methods, ACOFS confirms not just the selection of several prominent features, but also the scoring of a minimum number for the (ACOFS) it has main high-accuracy solutions for FS from a given datasets [3].…”

Section: Imentioning

confidence: 80%

“…•The work of [Ref. [3] notice above the proposed of Ant colony is used the nine types of dataset to obtain the good and fast velocity of select the subset of feature selection and to make sure the results in feature selection, used the proposed to select the best sets of large, medium, and small, while this algorithm is failed in some fields when is alone. But when using in the feature selection good results and high accuracy, it works used in many fields because it has highly efficient and low cost, this proposed can reduce the size of subset and select parameters it uses in the work, this proposed is a sensitive the change in the size of set, the work of ACOFS is one consider improvement in the future it used this algorithm because it easily and efficiency with compare other algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…A. MonirulKabir, MdShahjahan and Kazuyuki Murase, in 2013 [3], they presented a work for Ant colony optimization based feature selection, the performance the ACOFS is used the experiments is using eight of datasets in the context types of famous datasets including the breast cancer, glass, vehicle, thyroid, ionosphere, credit card, sonar, and gene is existed in UCI machine learning repository is used the method to select the subset of feature and reduced the dimensional the datasets and show the results of the experiments the is reduced the dimensional and obtained the optimal solution in the easy way, low cost and very fast, the work is it observed in the proposed the different groups is large, medium and small is have the high accuracy. The results of the low standard deviations of the average classification performances so the average number of chosen features, offered the robustness of this method.…”

Section: Literature Surveymentioning

confidence: 99%

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Intelligent Feature Selection Methods: A Survey

Jameel¹,

Abdullah²

2021

ETJ

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Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish algorithm, cat algorithm, Genetic algorithm ...etc. In feature selection because to increment fast, high accuracy and easy to use of user. In this paper survey it used the Some the swarm intelligent method: Ant colony, Bees colony, Cuckoo search, Particle optimization and Genetic algorithm (GA). It done take the related work for each algorithms the swarm intelligent the ideas, dataset and accuracy of the results after that was done to compare the result in the table among the algorithms and learning the better algorithm is discuses in the discussion and why it is better. Finally, it learning who is the advantage and disadvantage for each algorithms of swarm intelligent in feature selection.

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Section: Imentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Feature Selection Methods: A Survey

Jameel¹,

Abdullah²

2021

ETJ

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“…A feature selection method is proposed in [37], where mutual information is combined with ant colony optimization for better performance. A hybrid method of feature selection techniques is proposed in [38], where filter-and wrapper-based search techniques are combined to provide an effective balance between exploration and exploitation of ants in search. Another hybrid approach comprising a classifier and a filter is discussed in [14], where ACO is used as a classifier.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Image Diagnosis

Fahad

Tahir

Shahzad

et al. 2020

Scientific Programming

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Irrelevant and redundant features increase the computation and storage requirements, and the extraction of required information becomes challenging. Feature selection enables us to extract the useful information from the given data. Streaming feature selection is an emerging field for the processing of high-dimensional data, where the total number of attributes may be infinite or unknown while the number of data instances is fixed. We propose a hybrid feature selection approach for streaming features using ant colony optimization with symmetric uncertainty (ACO-SU). The proposed approach tests the usefulness of the incoming features and removes the redundant features. The algorithm updates the obtained feature set when a new feature arrives. We evaluate our approach on fourteen datasets from the UCI repository. The results show that our approach achieves better accuracy with a minimal number of features compared with the existing methods.

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“…Some of them use hybrid techniques (filter and wrapper) to estimate the heuristic information and overall performance. For example, Kabir et al [21,22] propose a new hybrid ACO algorithm for feature selection, called ACOFS. ACOFS uses a hybrid search technique that combines the advantages of wrapper and filter approaches.…”

Section: Aco Based Feature Selectionmentioning

confidence: 99%

Hybrid ACO-PSO Based Approaches for Feature Selection

Menghour¹,

Souici-Meslati²

2016

IJIES

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Abstract:Feature selection attempts to find the most discriminative information aiming to design an accurate learning system. Feature selection has been the focus of interest for a long time and many works had been done. Recently, the tendency of research in this domain is oriented to the bio-inspired methods. In this paper, we propose hybrid bio-inspired approaches applied to the feature selection problem. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with simple bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization and genetic algorithm. Our experimental results show the efficiency of the proposed approaches in the reduction of selected features number and improvement of classification performance.

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Ant Colony Optimization Toward Feature Selection

Cited by 12 publications

References 56 publications

Intelligent Feature Selection Methods: A Survey

Intelligent Feature Selection Methods: A Survey

Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Image Diagnosis

Hybrid ACO-PSO Based Approaches for Feature Selection

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