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A novel test-cost-sensitive attribute reduction approach using the binary bat algorithm

KNOWLEDGE-BASED SYSTEMS 2019年 186卷 104938-000页

作者： Xie, Xiaojun Qin, Xiaolin Zhou, Qian Zhou, Yanghao Zhang, Tong Janicki, Ryszard Zhao, Wei Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China McMaster Univ Dept Comp & Software Hamilton ON L8S 4K1 Canada Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing 210003 Jiangsu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

Attribute reductions are essential pre-processing steps in such as data mining, machine learning, pattern recognition and many other fields. Moreover, test-cost-sensitive attribute reductions are often used when we have to deal with cost-sensitive data. The main result of this paper is a new meta-heuristic optimization method for finding optimal test-cost-sensitive attribute reduction that is based on binary bat algorithm that originally was designed to model the echolocation behavior of bats when they search their prey. First we provide a 0-1 integer programming algorithm that can calculate optimal reduct but is inefficient for large data sets. We will use it to evaluate other algorithms. Next, a new fitness function that utilizes the pairs of inconsistent objects and does not have any uncertain parameter is design and an efficient algorithm for counting inconsistent pairs is provided. Then, an efficient test-cost-sensitive attribute reduction technique that uses binary bat algorithm is provided. Finally, a new evaluation model with four different evaluation metrics has been proposed and used to evaluate algorithms that only provide sub-optimal solutions. Several experiments were carried out on broadly used benchmark data sets and the results have shown the superiority of our new algorithm, in terms of various metrics, computational time, and classification accuracy, especially for high-dimensional data sets. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Rough set Attribute reduction Test-cost-sensitive binary bat algorithm

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Fetal state health monitoring using novel Enhanced binary bat algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Sharma, Prerna Sharma, Kapil Delhi Technol Univ Delhi India Delhi Technol Univ Informat Technol Delhi India

The timely assessment of hypoxic fetuses by cardiotocography is of utmost significance as deficiency of oxygen in fetuses' leads to fetal distress which can further prove to be fatal or cause neurological diseases. This paper puts forward an Enhanced binary bat algorithm, the altered form of binary bat algorithm for the multi-classification problem of Cardiotocography. Subset of optimal and relevant features is selected using the optimized and Enhanced BBA algorithm from cardiotocography dataset. Features selected by various evolutionary algorithms EBBA (Enhanced binary bat algorithm), qGWO (quantum Grey Wolf Optimization), Genetic algorithm are 11, 15, 12 simultaneously. EBBA efficiently selects most reduced set of features. The proposed EBBA can be used in feature selection and classification of cardiotocography dataset under different fetus state i.e. normal, suspect and pathologic with an accuracy of 96.21% using machine learning classifier i.e. random forest.

关键词： Cardiotocography Classification Feature selection Evolutionary algorithm bat algorithm binary bat algorithm Fetal distress

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A novel quantum-inspired binary bat algorithm for leukocytes classification in blood smear

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EXPERT SYSTEMS 2022年第3期39卷

作者： Sharma, Prerna Sharma, Kapil Delhi Technol Univ Comp Sci & Engn Delhi India Delhi Technol Univ Informat Technol Delhi India

In the field of medical sciences, day-to-day procedure is followed for identification of bone marrow and immune system related diseases, which is most of the time carried out manually. The notion is to perform differential and qualitative analysis of leukocytes for the timely diagnosis of these diseases. In this article, a systematized solution is offered for the classification of leukocytes in blood smear. The proposed model incorporates the optimistic aspects of nature-inspired and quantum-inspired algorithms;this model tends to be perfect blend of both the techniques. For reducing the dimensionality, that is, irrelevant features;the quantum-inspired binary bat algorithm (QBBA) has been used in the proposed model. The optimality of features selected has been computed with the help of accuracy measure using various machine learning classifiers like Logistic Regression, KNN, Random Forest, Decision Tree. The performance of QBBA and its customary algorithms has been compared and the results depict that QBBA outperforms binary bat algorithm for the same set of population. QBBA comes out as an influential algorithm with an average accuracy of 98.31% and also possess enhanced noise invulnerability. The proposed QBBA can also find its usage in thorough haematological analysis.

关键词： binary bat algorithm classification evolutionary algorithm feature selection leukocytes quantum-inspired binary bat algorithm quantum-inspired computing

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A wrapper based binary bat algorithm with greedy crossover for attribute selection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 187卷 115828-115828页

作者： Akila, S. Christe, S. Allin PSG Coll Technol Dept Elect & Commun Engn Coimbatore 641004 Tamil Nadu India

Attribute selection plays a vital role in optimization and machine learning that involves huge datasets. Classification accuracy of any learning model depends on the dimensionality of data and attributes selected. This leads to a multi-objective problem of obtaining high classification accuracy with fewer attributes. In this research work, a multi-objective optimization algorithm with greedy crossover for attribute selection and classification is proposed. A wrapper based binary bat algorithm (BBA) with Support Vector Machine (SVM) as evaluator is implemented for attribute selection. In general, the optimization algorithms have the tendency to prematurely converge with sub-optimal solutions. This reduces the quality of the attribute selected and efficiency of the algorithm. Here, a multi-objective binary bat algorithm with greedy crossover is proposed to reset the sub-optimal solutions that are obtained due to the premature convergence. The evaluation of the attributes selected is done using the Support Vector Machine with 10-fold cross-validation. The proposed algorithm is implemented and evaluated with the benchmark datasets available in the UC Irvine (UCI) repository. Classification accuracy of 89.25%, 96.45%, 96.57% and 88.50% using the Australian, Ionosphere, Wisconsin Breast Cancer (Original dataset) and Musk is obtained. Further analysis is made with parameter metrics like sensitivity, specificity, precision, recall, fmeasure, Matthews Correlation coefficient (MCC), confusion matrix and Area under the ROC Curve (AUC). The proposed multi-objective binary bat algorithm with greedy crossover yields better performance over the existing bat based algorithms and other nature-inspired algorithms. The solution for the multiobjective problem of obtaining high classification accuracy with minimal number of attributes is attained. Also, the problem of premature convergence occurring in the optimization algorithms with sub-optimal solutions is overcome using the proposed

关键词： Attribute selection Classification Nature-inspired algorithm SVM binary bat algorithm Wrapper based algorithms

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Gene selection and classification using correlation feature selection based binary bat algorithm with greedy crossover

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第5期34卷

作者： Seetharaman, Akila Sundersingh, Allin Christe PSG Coll Technol Dept Elect & Commun Engn Coimbatore 641004 Tamil Nadu India

DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly predictive gene features must be obtained without affecting the accuracy. Previous studies concentrated either on improving the classification accuracy or reduction of gene features. Here, the multi-objective problem of obtaining reduced gene features with high classification accuracy is addressed using the proposed correlation feature selection filter and binary bat algorithm (BBA) with greedy crossover. The gene feature subsets are obtained using the correlation based feature selection filter and optimized using the BBA. Suboptimal solutions obtained due to pre-convergence of BBA are reset using the proposed greedy crossover. Highly predictive genes features are obtained and evaluated with support vector machine 10-fold cross-validation. An average classification accuracy of 95.85% with predictive gene features <1% of the total dataset was obtained when applied on cancer microarray datasets. The solution for the multi-objective problem of obtaining high classification accuracy with minimal number of genes is achieved with better performance over the existing algorithms. Also, the problem of pre-convergence with suboptimal solutions in optimization algorithms is overcome.

关键词： binary bat algorithm cancer classification gene selection microarray classification nature inspired optimization algorithm SVM

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An Improved Chaotic binary bat algorithm for QoS Multicast Routing

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2016年第4期25卷 1650025-1650025页

作者： Meraihi, Yassine Acheli, Dalila Univ MHamed Bougara Boumerdes Automat Dept Appl Automat Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter beta of the pulse frequency f(i). Second, we use a dynamic formulation to update the parameter alpha of the loudness A(i). The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and binary bat algorithm (BBA).

关键词： bat algorithm binary bat algorithm quality of service (QoS) multicast routing chaotic maps

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A Novel Hybrid binary bat algorithm for Global Optimization

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2024年第1期15卷 1页

作者： Liang, Huijun Pang, Aokang Lin, Chenhao Zhong, Jianwei Hubei Minzu Univ Coll Intelligent Syst Sci & Engn Enshi Peoples R China Hubei Minzu Univ Enshi Peoples R China

In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time -varying v -shaped transfer function, rather than a time -invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

关键词： binary bat algorithm Random Black Hole Model Transfer Function Composition Benchmark Function Unit Commitment

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A discrete binary version of bat algorithm for multidimensional knapsack problem

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2014年第2期6卷 140-152页

作者： Sabba, Sara Chikhi, Salim Constantine Univ 2 Fac Engn Sci Dept Comp Sci Constantine 25017 Algeria

The nature has become a main inspiration source of scientists for developing new intelligent systems and techniques. Nature-inspired meta-heuristics is a kind of algorithms that imitate the social behaviour of some biological species. The bat algorithm (BA) is a new bio-inspired algorithm recently introduced by Yang (2010a). It is an optimisation method that is based on the echolocation behaviour of microbats. Firstly, the BA has been proposed for continuous problems. In this paper, we propose a discrete binary bat algorithm (BinBA) for solving the optimisation problems in binary space. The proposed algorithm is based on the sigmoid function used by Kennedy and Eberhart in 1997 for their binary particle swarm optimisation algorithm. The BinBA was tested on hard instances of the multidimensional knapsack problem. The obtained results are very promising compared to other bio-inspired algorithms.

关键词： discrete optimisation problem bio-inspired algorithm bat algorithm binary bat algorithm multidimensional knapsack problem MKP

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Comparative Analysis between binary Grey Wolf and binary bat Optimization for Optimal PMU Placement with Complete Observability 6

Comparative Analysis between Binary Grey Wolf and Binary Bat...

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6th IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)

作者： Priyadarshini, Subhashree Panigrahi, Chinmoy Kumar KIIT Deemed Univ Sch Elect Engn Bhubaneswar India

ISBN: (纸本)9781665419178

This paper presents the comparative analysis between the binary grey wolf optimization (BGWO) and the binary bat algorithm (BBA) to find the solution of the optimal PMU placement (OPP) problem by minimizing the number of phasor measurement units (PMUs) with maximum measurement redundancy to achieve complete observability of the power system. The proposed techniques have been implemented on various IEEE standard buses such as 14 bus, 30 bus, 57 bus and 118 bus system both under the normal condition as well as contingency condition like single PMU loss. The comparative results show that BGWO is more efficient than the BBA approach.

关键词： binary bat algorithm binary grey wolf optimization maximum redundancy observability optimal PMUplacement phasor measurement unit

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A binary bat Approach for Identification of Fatigue Condition from sEMG Signals 5th

A Binary Bat Approach for Identification of Fatigue Conditio...

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5th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Makaram, Navaneethakrishna Swaminathan, Ramakrishnan Indian Inst Technol Dept Appl Mech Non Invas Imaging & Diagnost Lab Madras 600036 Tamil Nadu India

ISBN: (纸本)9783319202945;9783319202938

In this work, an attempt has been made to investigate the effectiveness of binary bat algorithm as a feature selection method to classify sEMG signals under fatigue and nonfatigue conditions. The sEMG signals are recorded from the biceps brachii muscle of 50 healthy volunteers. The signals are preprocessed and then multiscale Renyi entropy based feature are extracted. The binary bat algorithm is used for feature selection and the effectiveness is compared with information gain based ranker. The performance of the feature selection algorithms are validated by performing classification using Naive Bayes, and least square support vector machines. The results show a decreasing trend in the multiscale Renyi entropy with increase in scale. Additionally, higher entropy values where observed in fatigue condition. The classification results showed that a maximum accuracy of 86.66 % is obtained with least square SVM and binary bat algorithm. It appears that, this technique is useful in identifying muscle fatigue in varied clinical conditions.

关键词： sEMG Fatigue Muscle binary bat algorithm Information gain LSSVM Naive Bayes

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