Feature Selection Based on Ant Colony Optimization for Image Classification

2013 ◽  
Vol 319 ◽  
pp. 337-342
Author(s):  
Li Tu ◽  
Li Zhi Yang
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Image Data ◽  
Ant Colony ◽  
Data Sets ◽  
Classification Rules ◽  
Selection Algorithm ◽  
Artificial Ants

In this paper, a feature selection algorithm based on ant colony optimization (ACO) is presented to construct classification rules for image classification. Most existing ACO-based algorithms use the graph with O(n2) edges. In contrast, the artificial ants in the proposed algorithm FSC-ACO traverse on a feature graph with only O(n) edges. During the process of feature selection, ants construct the classification rules for each class according to the improved pheromone and heuristic functions. FSC-ACO improves the qualities of rules depend on the classification accuracy and the length of rules. The experimental results on both standard and real image data sets show that the proposed algorithm can outperform the other related methods with fewer features in terms of speed, recall and classification accuracy.

A Hybrid Feature Selection Based on Ant Colony Optimization and Probabilistic Neural Networks for Bearing Fault Diagnostics

2007 ◽  
Vol 10-12 ◽  
pp. 573-577
Author(s):  
Y.H. Gai ◽  
Gang Yu
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Ant Colony Optimization ◽  
Feature Selection Method ◽  
Ant Colony ◽  
Diagnostic Purpose ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Probabilistic Neural Networks ◽  
Bearing Fault

This paper presents a novel hybrid feature selection algorithm based on Ant Colony Optimization (ACO) and Probabilistic Neural Networks (PNN). The wavelet packet transform (WPT) was used to process the bearing vibration signals and to generate vibration signal features. Then the hybrid feature selection algorithm was used to select the most relevant features for diagnostic purpose. Experimental results for bearing fault diagnosis have shown that the proposed hybrid feature selection method has greatly improved the diagnostic performance.

MLACO: A multi-label feature selection algorithm based on ant colony optimization

2020 ◽  
Vol 192 ◽  
pp. 105285 ◽  
Author(s):  
Mohsen Paniri ◽  
Mohammad Bagher Dowlatshahi ◽  
Hossein Nezamabadi-pour
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

scholarly journals An Improved Feature Selection Algorithm Based on Ant Colony Optimization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69203-69209 ◽  
Author(s):  
Huijun Peng ◽  
Chun Ying ◽  
Shuhua Tan ◽  
Bing Hu ◽  
Zhixin Sun
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

Ant Colony Optimization for Feature Selection Involving Effective Local Search

2011 ◽  
Vol 15 (6) ◽  
pp. 671-680
Author(s):  
Md. Monirul Kabir ◽  
◽  
Md. Shahjahan ◽  
Kazuyuki Murase ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Ant Colony Optimization ◽  
Classification Accuracy ◽  
Search Strategy ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Exploration And Exploitation ◽  
Algorithm Performance

This paper proposes an effective algorithm for feature selection (ACOFS) that uses a global Ant Colony Optimization algorithm (ACO) search strategy. To make ACO effective in feature selection, our proposed algorithm uses an effective local search in selecting significant features. The novelty of ACOFS lies in its effective balance between ant exploration and exploitation using new pheromone update and heuristic information computation rules to generate a subset of a smaller number of significant features. We evaluate algorithm performance using seven real-world benchmark classification datasets. Results show that ACOFS generates smaller subsets of significant features with improved classification accuracy.

An improved feature selection algorithm based on graph clustering and ant colony optimization

2018 ◽  
Vol 159 ◽  
pp. 270-285 ◽  
Author(s):  
Hojat Ghimatgar ◽  
Kamran Kazemi ◽  
Mohamamd Sadegh Helfroush ◽  
Ardalan Aarabi
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Graph Clustering ◽  
Ant Colony ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

Design of Image Classification System Based on Feature Extraction

2011 ◽  
Vol 474-476 ◽  
pp. 1859-1864
Author(s):  
Guang Nan Guo ◽  
Mei Chu ◽  
Xiao Hua Wang ◽  
Xiao Bo Huang ◽  
Zheng Wei
Keyword(s):  
Data Mining ◽  
Feature Extraction ◽  
Feature Selection ◽  
Image Classification ◽  
Classification System ◽  
Classification Accuracy ◽  
Image Data ◽  
Mining Method ◽  
Feature Weight ◽  
Implementation Method

Image classification is a kind of image data mining method to classify different targets based on different features reflected in image information. The paper designed a kind of image classification system based on feature selection, which utilize feature selection and feature weight to optimize the features and obtain features that can reflect essential of classification, so as to improve image classification accuracy. Meanwhile, the paper gave material implementation method of main modules of image classification system. Image classification experiment based on the system proves effectiveness of the designed system.

scholarly journals High-Accuracy Power Quality Disturbance Classification Using the Adaptive ABC-PSO as Optimal Feature Selection Algorithm

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1238
Author(s):  
Supanat Chamchuen ◽  
Apirat Siritaratiwat ◽  
Pradit Fuangfoo ◽  
Puripong Suthisopapan ◽  
Pirat Khunkitti
Keyword(s):  
Feature Selection ◽  
Power Quality ◽  
Distribution System ◽  
Classification Accuracy ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Electrical Distribution ◽  
Power Quality Disturbance ◽  
Optimal Feature Selection ◽  
Optimal Feature

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

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