scholarly journals A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3456
Author(s):  
Chunlin He ◽  
Yong Zhang ◽  
Dunwei Gong
Keyword(s):  
Classification Accuracy ◽  
Artificial Bee Colony ◽  
Computational Cost ◽  
Band Selection ◽  
Selection Algorithm ◽  
Evolutionary Clustering ◽  
Bee Colony ◽  
Hyperspectral Remote Sensing Images ◽  
Selection Algorithms ◽  
Grid Division

Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant and noise bands. Firstly, replacing traditional pixel points by super-pixel centers, a hypergraph evolutionary clustering method with low computational cost is developed to generate high-quality pseudo-labels; Then, on the basis of these pseudo-labels, taking classification accuracy as the optimized objective, a supervised band selection algorithm based on artificial bee colony is proposed. Moreover, a noise filtering mechanism based on grid division is designed to ensure the accuracy of pseudo-labels. Finally, the proposed algorithm is applied in 3 real datasets and compared with 6 classical band selection algorithms. Experimental results show that the proposed algorithm can obtain a band subset with high classification accuracy for all the three classifiers, KNN, Random Forest, and SVM.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Evaluation Method for Band Selection Algorithms of Hyperspectral Image

2013 ◽  
Vol 684 ◽  
pp. 495-498
Author(s):  
Bai He Wang ◽  
Shi Qi Huang ◽  
Yi Hong Li
Keyword(s):  
Data Fusion ◽  
Dimension Reduction ◽  
Evaluation Method ◽  
Hyperspectral Image ◽  
Band Selection ◽  
Selection Algorithm ◽  
Amount Of Information ◽  
Algorithm Evaluation ◽  
Common Band ◽  
Selection Algorithms

Band selection algorithm is most important in data dimension reduction of hyperspectral image. There are many algorithms of band selection, but there are only few methods to do algorithm evaluation. A method is put forward in this paper to evaluate the band selection algorithm of hyperspectral image. The amount of information, brightness, image contrast and definition are defined as 4 indexes to measure deferent data fusion based on various band selection results. Based on the measurement, the evaluation of band selection algorithm is realized. In the paper, the evaluation method is used in the compare of 4 common band selection algorithms, the result of measurement is analyzed and the feasibility is verified.

scholarly journals Performance Evaluation of Feature Selection Algorithms Applied to Online Learning in Concept Drift Environments

2018 ◽  
Author(s):  
Matheus B. De Moraes ◽  
André L. S. Gradvohl
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Concept Drift ◽  
Computational Cost ◽  
Selection Algorithm ◽  
Information Need ◽  
High Speeds ◽  
Online Feature Selection ◽  
Classification Tasks ◽  
Selection Algorithms

Data streams are transmitted at high speeds with huge volume and may contain critical information need processing in real-time. Hence, to reduce computational cost and time, the system may apply a feature selection algorithm. However, this is not a trivial task due to the concept drift. In this work, we show that two feature selection algorithms, Information Gain and Online Feature Selection, present lower performance when compared to classification tasks without feature selection. Both algorithms presented more relevant results in one distinct scenario each, showing final accuracies up to 14% higher. The experiments using both real and artificial datasets present a potential for using these methods due to their better adaptability in some concept drift situations.

Artificial Bee Colony–Based Feature Selection Algorithm for Cyberbullying

2020 ◽  
Author(s):  
Esra Sarac Essiz ◽  
Murat Oturakci
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Feature Selection Method ◽  
Classification Performance ◽  
Selection Method ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Traditional Methods ◽  
Bee Colony ◽  
Nature Inspired Algorithm

Abstract As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

scholarly journals Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhihuan Liu
Keyword(s):  
Shortest Path ◽  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Effect ◽  
Path Selection ◽  
Cold Chain ◽  
Selection Algorithm ◽  
Selection Accuracy ◽  
Bee Colony

Aiming at the problems of low shortest path selection accuracy, longer response time, and poor selection effect in current cold chain logistics transportation methods, a cold chain logistics transportation shortest path selection algorithm based on improved artificial bee colony is proposed. The improved algorithm is used to initialize the food source, reevaluate the fitness value of the food source, generate a new food source, optimize the objective function and food source evaluation strategy, and get an improved artificial bee colony algorithm. Based on the improved artificial bee colony algorithm, the group adaptive mechanism of particle swarm algorithm is introduced to initialize the position and velocity of each particle randomly. Dynamic detection factor and octree algorithm are adopted to dynamically update the path of modeling environment information. According to the information sharing mechanism between individual particles, the group adaptive behavior control is performed. After the maximum number of cycles, the path planning is completed, the shortest path is output, and the shortest path selection of cold chain logistics transportation is realized. The experimental results show that the shortest path selection effect of the cold chain logistics transportation of the proposed algorithm is better, which can effectively improve the shortest path selection accuracy and reduce the shortest path selection time.

Improved Artificial Bee Colony Algorithm and its Application in Classification

2018 ◽  
Vol 30 (6) ◽  
pp. 921-926
Author(s):  
Haiquan Wang ◽  
Jianhua Wei ◽  
Shengjun Wen ◽  
Hongnian Yu ◽  
Xiguang Zhang ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Initial Solution ◽  
Solution Set ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Parameters Selection ◽  
Optimal Feature

For improving the classification accuracy of the classifier, a novel classification methodology based on artificial bee colony algorithm is proposed for optimal feature and SVM parameters selection. In order to balance the ability of exploration and exploitation of traditional ABC algorithm, improvements are introduced for the generation of initial solution set and onlooker bee stage. The proposed algorithm is applied to four datasets with different attribute characteristics from UCI and efficiency of the algorithm is proved from the results.

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