Open AccessProceedings Article
Enhanced feature selection algorithm using Ant Colony Optimization and fuzzy memberships
Rami N. Khushaba,Akram AlSukker,Ahmed Al-Ani,Adel Al-Jumaily +3 more
- 06 Feb 2008
- pp 34-39
TL;DR: A novel feature selection method that utilizes both the Ant Colony Optimization and fuzzy memberships is presented, which can be as accurate as the original method with MI, but with a significant reduction in computational cost, especially when dealing with huge datasets.
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Abstract: Feature selection is an indispensable pre-processing step when mining huge datasets that can significantly improve the overall system performance. This paper presents a novel feature selection method that utilizes both the Ant Colony Optimization (ACO) and fuzzy memberships. The algorithm estimates the local importance of subsets of features, i.e., their pheromone intensities by utilizing fuzzy c-means (FCM) clustering technique. In order to prove the effectiveness of the proposed method, a comparison with another powerful ACO based feature selection algorithm that utilizes the Mutual Information (MI) concept is presented. The method is tested on two biosignals driven applications: Brain Computer Interface (BCI), and prosthetic devices control with myoelectric signals (MES). A linear discriminant analysis (LDA) classifier is used to measure the performance of the selected subsets in both applications. Practical experiments prove that the new algorithm can be as accurate as the original method with MI, but with a significant reduction in computational cost, especially when dealing with huge datasets.
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Citations
A new hybrid ant colony optimization algorithm for feature selection
TL;DR: A new hybrid ant colony optimization (ACO) algorithm for feature selection (FS), called ACOFS, using a neural network, has a remarkable ability to generate reduced-size subsets of salient features while yielding significant classification accuracy.
271
An Efficient Feature Selection Using Ant Colony Optimization Algorithm
Md. Monirul Kabir,Md. Shahjahan,Kazuyuki Murase +2 more
- 15 Dec 2009
TL;DR: The experimental results show that ACOFS has a remarkable capability to generate reduced size subsets of salient features with yielding significant classification accuracies.
22
BSO-FS: Bee Swarm Optimization for Feature Selection in Classification
Souhila Sadeg,Leila Hamdad,Karima Benatchba,Zineb Habbas +3 more
- 10 Jun 2015
TL;DR: The proposed algorithm is based on the wrapper approach that uses BSO for the generation of feature subsets, and a classifier algorithm to evaluate the solutions, and results show that for the majority of datasets, BSO-FS selects efficiently relevant features while improving the classification accuracy.
16
•Journal Article
Unsupervised gene selection and clustering using simulated annealing
TL;DR: In this paper, a wrapper method for gene selection based on simulated annealing and unsupervised clustering is proposed, even if computationally intensive, permits to select the most relevant features (genes) and to rank their relevance, allowing to improve the results of clustering algorithms.
15
Ant Colony Optimization Toward Feature Selection
Monirul Kabir,Shahjahan,Kazuyuki Murase +2 more
- 20 Feb 2013
TL;DR: A number of good outcomes can be expect‐ ed from the applications, such as, speeding up data mining algorithms, improving mining performances (including predictive accuracy) and comprehensibility of result.
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The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances
Marco Dorigo,Thomas Stützle +1 more
- 01 Jan 2003
TL;DR: The field of ACO algorithms is very lively, as testified, for example, by the successful biannual workshop (ANTS—From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms; http://iridia.ulb.ac.be/~ants/) where researchers meet to discuss the properties ofACO and other ant algorithms.
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A Comparison of Surface and Intramuscular Myoelectric Signal Classification
TL;DR: This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which UseMulti-channel intramuscular M ES as inputs.
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Ant colony system for a dynamic vehicle routing problem
TL;DR: A solving strategy, based on the Ant Colony System paradigm, is proposed for dynamic vehicle routing problems, where new orders are received as time progresses and must be dynamically incorporated into an evolving schedule.