Fuzzy kernel feature selection with multi-objective differential evolution algorithm

In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response

A new approach to the reconstruction of contour lines extracted from topographic maps

It is known that after segmentation and morphological operations on topographic maps, gaps occur in contour lines. It is also well known that filling these gaps and reconstruction of contour lines with high accuracy is not an easy problem. In this paper, a nontrivial semi-automatic approach to solve this problem is proposed. The main idea of the proposed approach is based on local and geometric properties such as (1) parabolic and opposite directions, (2) the differences of y-ordinate of end points, (3) changing the directions of x-axis and y-ordinate to the nearest clockwise direction and (4) avoiding the use of the second end point of a small piece of any contour line in the same mask if its other end point is used. The proposed approach was implemented on the base of many topographic maps with different resolutions and complexity. The obtained results show that the proposed approach increases accuracy and performance. (c) 2012 Elsevier Inc. All rights reserved

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

Today's data mostly does not include the knowledge of cluster number. Therefore, it is not possible to use conventional clustering approaches to partition today's data, i.e., it is necessary to use the approaches that automatically determine the cluster number or cluster structure. Although there has been a considerable attempt to analyze and categorize clustering algorithms, it is difficult to find a survey paper in the literature that has thoroughly focused on the determination of cluster number. This significant issue motivates us to introduce concepts and review methods related to automatic cluster evolution from a theoretical perspective in this study. (C) 2016 Elsevier B.V. All rights reserved

A novel binary artificial bee colony algorithm based on genetic operators

This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stages: (1) In neighbourhood of a (current) food source, randomly select two food sources from population and generate a solution including zeros (Zero) outside the population; (2) apply two-point crossover operator between the current, two neighbourhood, global best and Zero food sources to create children food sources; (3) apply swap operator to the children food sources to generate grandchildren food sources; and (4) select the best food source as a neighbourhood food source of the current solution among the children and grandchildren food sources. In this way, the global-local search ability of the basic ABC algorithm is improved in binary domain. The effectiveness of the proposed algorithm GB-ABC is tested on two well-known binary optimization problems: dynamic image clustering and 0-1 knapsack problems. The obtained results clearly indicate that GB-ABC is the most suitable algorithm in binary optimization when compared with the other well-known existing binary optimization algorithms. In addition, the achievement of the proposed algorithm is supported by applying it to the CEC2005 benchmark numerical problems. (C) 2014 Elsevier Inc. All rights reserved

Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm

Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others

A Hybrid Method to the Reconstruction of Contour Lines from Scanned Topographic Maps

This paper addresses with the problem of contour line reconstruction extracted from scanned topographic maps since contour lines play a significant role on construction of Digital Evaluation Models (DEMs) and 3D simulations in serious fields. In this way, a semi-automatic hybrid method based on simple geometrical properties is proposed. The proposed hybrid method is designed by combining the two previously presented methods such as advanced and highly advanced methods in the literature. The contribution of the proposed hybrid method is to increase the accuracy of the advanced method and is to improve the run-time of highly advanced method in the process of reconstruction. The effectiveness of the algorithm is demonstrated by comparing it with the five popular methods in the literature. The implementation results show that the proposed hybrid method outperforms the others and can be efficiently employed in reconstruction process of contour lines

ARTIFICIAL BEE COLONY BASED IMAGE CLUSTERING METHOD

Clustering plays important role in many areas such as medical applications, pattern recognition, image analysis and statistical data analysis. Image clustering is an application of image analysis in order to support high-level description of image content for image understanding where the goal is finding a mapping of the images into clusters. This paper presents an Artificial Bee Colony (ABC) based image clustering method to find clusters of an image where the number of clusters is specified. The proposed method is applied to three benchmark images and the performance of it is analysed by comparing the results of K-means and Particle Swarm Optimization (PSO) algorithms. The comprehensive results demonstrate both analytically and visually that ABC algorithm can be successfully applied to image clustering

AUTOMATIC CLUSTERING WITH GLOBAL BEST ARTIFICIAL BEE COLONY ALGORITHM

Clustering, which is an important technique in analyzing data, is used in many fields, especially in image processing and statistical data analysis. In recent years, studies particularly on solving the clustering problem have been increased. In this paper, the global search ability of the artificial bee colony algorithm is improved and a vectorial search ability is integrated to the algorithm in order to solve the automatic clustering problem. The proposed clustering method is tested on the well- known benchmark datasets and images. The obtained results show that the performance of the proposed method is superior to the others and it can be applied to the automatic clustering problems

Dynamic clustering with improved binary artificial bee colony algorithm

One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is accepted as one of the simple, novel and efficient binary variant of the artificial bee colony, the applied mechanism for generating new solutions concerning to the information of similarity between the solutions only consider one similarity case i.e. it does not handle all similarity cases. To cover this issue, new solution generation mechanism of the discrete artificial bee colony is enhanced using all similarity cases through the genetically inspired components. Furthermore, the superiority of the proposed algorithm is demonstrated by comparing it with the basic discrete artificial bee colony, binary particle swarm optimization, genetic algorithm in dynamic (automatic) clustering, in which the number of clusters is determined automatically i.e. it does not need to be specified in contrast to the classical techniques. Not only evolutionary computation based algorithms, but also classical approaches such as fuzzy C-means and K-means are employed to put forward the effectiveness of the proposed approach in clustering. The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers. (C) 2014 Elsevier B.V. All rights reserved

A binary ABC algorithm based on advanced similarity scheme for feature selection

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers' attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection. (C) 2015 Elsevier B.V. All rights reserved