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A Hybrid Feature Selection algorithm Based on a Discrete artificial bee colony for Parkinson's Diagnosis

ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2021年第3期21卷 1–22页

作者： Li, Haolun Pun, Chi-Man Xu, Feng Pan, Longsheng Zong, Rui Gao, Hao Lu, Huimin Nanjing Univ Posts & Telecommun Coll Automat 9 Wenyuan Rd Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence 9 Wenyuan Rd Nanjing 210023 Peoples R China Univ Macau Dept Comp & Informat Sci Ave Univ Taipa 999078 Macau Sar Peoples R China Tsinghua Univ Sch Software 30 Shuangqing Rd Beijing 100084 Peoples R China Peoples Liberat Army Gen Hosp Dept Neurosurg 28 Fuxing Rd Beijing 100853 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn 1-1 Xianshui Rd Kitakyushu Fukuoka 8048550 Japan

Parkinson's disease is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the computational burden. To ensure the optimal classification performance, this article proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection. The algorithm combines the advantages of filters and wrappers to eliminate most of the uncorrelated or noisy features and determine the optimal subset of features. In the filter, three different variable ranking methods are employed to pre-rank the candidate features, then the population of artificial bee colony is initialized based on the significance degree of the re-rank features. In the wrapper part, the artificial bee colony algorithm evaluates individuals (feature subsets) based on the classification accuracy of the classifier to achieve the optimal feature subset. In addition, for the first time, we introduce a strategy that can automatically select the best classifier in the search framework more quickly. By comparing with several publicly available datasets, the proposed method achieves better performance than other state-of-the-art algorithms and can extract fewer effective features.

关键词： Parkinson's disease machine learning feature extraction artificial bee colony algorithm

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Fuzzy optimal power flow with multi-objective based on artificial bee colony algorithm in power system

Fuzzy optimal power flow with multi-objective based on artif...

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International Conference on Renewable Energy and Environmental Technology (REET 2013)

作者： He, Xuanhu Wang, Wei Wang, Yingnan Kong, Jun Geng, Jing Fan, Shengbin Beijing JiaoTong Univ Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9783037859124

According to the problem of optimal power flow (OPF), the optimization objectives including minimization of total fuel cost for generating units, minimization of emission for atmospheric pollutants, minimization of active power losses and minimization of voltages deviations are established. The paper uses fuzzy membership functions instead of multi-objective functions to form fuzzy optimal power flow in the optimal power flow calculation process. The novel artificial bee colony (ABC) algorithm is proposed to solve OPF problem with multi-objective. The proposed approach is applied to the OPF problem on IEEE30 test systems. And the simulation results verify the effectiveness of the proposed method.

关键词： Power System Optimal Power Flow Fuzzy Mathematics Multi-objective Function artificial bee colony algorithm

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Classifying Piper Nigrum using artificial bee colony algorithm 4

Classifying Piper Nigrum using Artificial Bee Colony Algorit...

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4th IEEE International Conference on artificial Intelligence with applications in Engineering and Technology (ICAIET)

作者： Yusup, Norfadzlan Iskandar, D. N. F. Awang Hassan, Siti Hazemah Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan 94300 Sarawak Malaysia

ISBN: (纸本)9781479979103

Pepper grading is one of the key elements in assuring the quality of the traded pepper. Currently, the pepper grading process performed by Malaysia Pepper Board (MPB) is semi-automated where a commercial colour sorter is used to distinguish white premium pepper and this grading process is very time consuming. In this paper we present the framework for pepper grading using the artificial bee colony algorithm (ABC) to classify the white pepper berries from digital images. Our initial experiment on the image features indicates that colour, shape and textures could be used as the parameters for the ABC algorithm for classifying the pepper berries into its respective grades.

关键词： artificial bee colony algorithm image processing and classification agricultural science

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An eXtreme Gradient Boosting algorithm Combining artificial bee colony Parameters Optimized Technique for Single Sand Body Identification

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IEEE ACCESS 2021年 9卷 156894-156906页

作者： Luo, Renze Guo, Liang Li, Xingyu Tuo, Juanjuan Lei, Canru Zhou, Yang Southwest Petr Univ Sch Earth Sci & Technol State Key Lab Oil & Gas Reservoir Geol & Exploit Chengdu 610500 Peoples R China Southwest Petr Univ Sch Earth Sci & Technol Chengdu 610500 Peoples R China

Due to the problems of traditional artificial single sand body identification methods such as strong subjectivity, heavy workload and low efficiency, we propose a fast and objective ABC-XGBoost. The algorithm consists of two parts: eXtreme gradient boosting (XGBoost) and artificial bee colony algorithm (ABC). XGBoost introduces a regular term, which can effectively prevent overfitting, and uses the second derivative to make the identification result more accurate. However, a large number of parameters in XGBoost need to be adjusted manually, which affects the efficiency of the algorithm. In this regard, ABC is used to optimize the parameters based on XGBoost, and then the single sand body can be identified quickly and effectively. We take the C6 21 oil-bearing layer in the second area of Dalugou, Jing'an Oilfield as the research object, and use the ABC-XGBoost to identify the single sand body in the research area. Based on the reasonable selection of physical parameter data and logging data, the partition and interlayer data should be eliminated first to avoid data redundancy. The results indicate that ABC-XGBoost is more efficient and accurate than the existing mainstream machine algorithms, such as support vector machines (SVM), random forests (RF), and XGBoost using trial and error tuning under the same logging data and computer hardware conditions. The accuracy can reach 90.6%, which has certain practical application value in the middle and late development of oil and gas fields.

关键词： Single sand body identification artificial bee colony algorithm machine learning eXtreme gradient boosting ABC-XGBoost

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A Stereo Remote Sensing Feature Selection Method Based on artificial bee colony algorithm

A Stereo Remote Sensing Feature Selection Method Based on Ar...

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Conference on Satellite Data Compression, Communications, and Processing X

作者： Yan, Yiming Liu, Pigang Zhang, Ye Su, Nan Tian, Shu Gao, Fengjiao Shen, Yi Harbin Inst Technol Harbin Heilongjiang Peoples R China Inst Automat Heilongjiang Acad Sci Heilongjiang Peoples R China

ISBN: (纸本)9781628410617

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

关键词： Remote sensing digital surface model classification feature selection artificial bee colony algorithm

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An artificial bee colony algorithm for multiobjective redundancy allocation problem in repairable system

An artificial bee colony algorithm for multiobjective redund...

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International Forum on Computer and Information Technology (IFCIT)

作者： He, Qifang Wang, Zutong Zheng, Mingfa Yan, Long Air Force Engn Univ Informat & Nav Coll Xian 710051 Peoples R China

ISBN: (纸本)9783038350194

This paper proposed a multiobjective artificial bee colony algorithm (MOABC) to solve the reliability redundancy allocation problem (RAP) with multiple objectives, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. The number of redundancy components is to be decided so as to maximize the availability and minimize the designing cost of the system simultaneously. It shows that the proposed algorithm can solve multiobjective redundancy allocation problem efficiently.

关键词： Redundancy allocation problem Series-parallel systems Multiobjctive optimization artificial bee colony algorithm

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Routing in wireless sensor network using artificial bee colony algorithm

Routing in wireless sensor network using Artificial Bee Colo...

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International Conference on Wireless Communication and Sensor Network (WCSN)

作者： Zheng, Wei Luo, Di Nanchang Hang Kong Univ Sch Software Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781479970919

This paper presents an intelligent approach for modeling Routing in wireless sensor networks. A new routing protocol based on artificial bee colony algorithm is introduced in this paper for solving delay-energy trade-off problem. In the proposed approach every food source represents a possible and feasible candidate path between each original and destination node. The positions of food sources are modified by some artificial bees in the population in order to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. Obtained simulation results show that the proposed protocol can achieve good trade-off between routing delay and energy.

关键词： wireless sensor networks artificial bee colony algorithm trade-off

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An Optimal Probabilistic Transformation of Belief Functions Based on artificial bee colony algorithm

An Optimal Probabilistic Transformation of Belief Functions ...

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10th International Conference on Intelligent Computing (ICIC)

作者： Song, Yafei Wang, Xiaodan Lei, Lei Xue, Aijun Air Force Engn Univ Sch Air & Missile Def Xian 710051 Peoples R China

ISBN: (纸本)9783319093338;9783319093321

One of the key issues in the applications of Dempster-Shafer evidence theory is how to make a decision based on Basic Probability Assignment (BPA). Besides the widely known pignistic transformation of belief functions, another conventional method is Plausibility Transformation, which transforms BPA to probability distribution by normalizing plausibility function of every singleton proposition. However, these two methods are stuck with the problem of impreciseness. To overcome this drawback, a new transformation method based on artificial bee colony algorithm is proposed. The obtained probability has the maximum correlation coefficient with the original BPA. Numerical examples are used to illustrate the efficiency of the proposed method.

关键词： Belief function Dempster-Shafer theory Probabilistic transformation Correlation Coefficient artificial bee colony algorithm

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An Improved Adaptive artificial bee colony algorithm

An Improved Adaptive Artificial Bee Colony Algorithm

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8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： He, Liying Bai, Qingyuan Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China

ISBN: (纸本)9783642549243;9783642549236

In order to overcome the defects of slow convergence speed and low precision appeared in the original artificial bee colony (ABC) algorithm, a novel and improved adaptive ABC algorithm is presented in this paper. By dynamically adapting the step length that controls the range of neighborhood during the process of search, the proposed algorithm produces three candidate solutions that have good performances in exploiting in small search space, exploring in large search space and remaining initial search space, respectively. For illustration, a single variable function is utilized to analyze the cause of low precision and slow convergence speed. In addition, a different probability selection strategy is introduced to maintain population diversity of the bee colony. The improved ABC algorithm is tested on five numerical optimization functions and compared with the original ABC algorithm and a novel ABC algorithm known as ABC-SAM. The results show that the improved ABC algorithm is superior to two other algorithms on convergence and optimization precision.

关键词： artificial bee colony algorithm Adaptive Swarm intelligence Function optimization

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A Novel artificial bee colony algorithm 6

A Novel Artificial Bee Colony Algorithm

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Yi, Yujiang He, Renjie Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China

ISBN: (纸本)9781479949557

artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies, we propose a novel artificial bee colony algorithm (NABC) for numerical optimization problems in this paper to improve the exploitation capability by incorporating the current best solution into the search procedure. Experiments are conducted on a set of unimodal/multimodal benchmark functions. The experiments results of NABC have been compared with Gbest-guided artificial bee colony algorithm (G-ABC), improved artificial bee colony algorithm (I-ABC), Elitist artificial bee colony algorithm (E-ABC). The results show that NABC is superior to those algorithms in most of the tested functions.

关键词： swarm intelligence artificial bee colony algorithm numerical function optimization

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