Weighted bee colony algorithm for discrete optimization problems with application to feature selection

Moayedikia, Alireza; Jensen, Richard; Wiil, Uffe Kock; Forsati, Rana

doi:10.1016/j.engappai.2015.06.003

Cited by 35 publications

(11 citation statements)

References 39 publications

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“…The main idea in EACO is to embed the edges that were previously traversed in the earlier executions. Moayedikia et al 10 introduced a weighted bee colony optimization for the feature selection (FS-wBCO). The authors used global and local weights to measure the importance of the features and also a new recruiter selection procedure.…”

Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

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GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Guendouzi¹,

Boukra²

2017

IJCIS

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Feature selection is used as a preprocessing step in the resolution of many problems using machine learning. It aims to improve the classification accuracy, speed up the model generation process, reduce the model complexity and reduce the required storage space. Feature selection is an NP-hard combinatorial optimization problem. It is the process of selecting a subset of relevant, non-redundant features from the original ones. Among the works that are proposed to solve this problem, few are dedicated for intrusion detection. This paper presents a new feature selection approach for intrusion detection, using the Biogeography Based Optimization (BBO) algorithm. The approach which is named Guided Adaptive Binary Biogeography Based Optimization (GAB-BBO) uses the evolutionary state estimation (ESE) approach and a new migration and mutation operators. The ESE approach we propose in this paper uses the Hamming distance between the binary solutions to calculate an evolutionary factor f which determines the population diversity. During this process, fuzzy logic is used through a fuzzy classification method, to perform the transition between the numerical f value and four evolutionary states which are : convergence, exploration, exploitation and jumping out. According to the state identified, GAB-BBO adapts the algorithm behavior using a new adaptive strategy. The performances of GAB-BBO are evaluated on benchmark functions and the Kdd'99 intrusion detection dataset. In addition, we use other different datasets for further validation. Comparative study with other algorithms is performed and the results show the effectiveness of the proposed approach.

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Section: Overview Of Feature Selection Methodsmentioning

confidence: 99%

“…The weight which is calculated in Eq. (6) was proposed for a Bee Colony Optimization (BCO) algorithm 10 to measure the acceptance degree of each source food and to compute the loyalty assessment. However, the authors use it in a different context than the one we propose.…”

Section: Weighted Local Search (Wls)mentioning

confidence: 99%

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Guendouzi¹,

Boukra²

2017

IJCIS

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“…The behavior of a bee in nature is the main inspiration for the BCO algorithm. This methodology is based on creation of multi agent system (colony of artificial bees) capabilities to successfully solve difficult combinatorial optimization problems [43][44][45]. BCO was proposed by Teodorović in 2001.…”

Section: Original Bee Colony Optimization Algorithm (Bco)mentioning

confidence: 99%

Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms

et al. 2018

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This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.

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“…Then the fitness (denoted f () in Algorithm 1) of this NHV is evaluated (lines 20 -21), where the vector will replace the worst vector of HM provided that the fitness of the newly generated harmony vector is better than worst fitness of HM. The fitness function can be classification accuracy, kappa statistics, G-Mean or any statistical measure such as Wilcoxon [8,9,23,36,38].…”

Section: Symonmentioning

confidence: 99%

“…SYMON displays similar performance across three data sets: LEU, SRBCT and LUG, with the values of 0.875, 1 and Underpinning SYMON's feature selection strategy is a meta-heuristic optimisation algorithm capable of finding the near-optimal part of the solution space. We opted for near-optimality as it is often hard and at times impossible to locate the optimal solution [38,39]. Nevertheless, this is sufficient to deliver SYMON a subset of features that is better than the compared works.…”

Section: Classifier Metricsmentioning

confidence: 99%

Feature selection for high dimensional imbalanced class data using harmony search

Moayedikia

Ong

Boo

et al. 2017

Engineering Applications of Artificial Intelligence

Self Cite

133

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Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks.authorsversionPeer reviewe

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Weighted bee colony algorithm for discrete optimization problems with application to feature selection

Cited by 35 publications

References 39 publications

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

GAB-BBO: Adaptive Biogeography Based Feature Selection Approach for Intrusion Detection

Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms

Feature selection for high dimensional imbalanced class data using harmony search

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