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Fault diagnosis method based on supervised particle swarm optimization classification algorithm

INTELLIGENT DATA ANALYSIS 2018年第1期22卷 191-210页

作者： Zheng, Bo Huang, Hong-Zhong Guo, Wei Li, Yan-Feng Mi, Jinhua Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China Civil Aviat Flight Univ China Guanghan 618307 Sichuan Peoples R China

A novel supervised particle swarm optimization (S-PSO) classification algorithm is proposed for fault diagnosis. In order to improve the accuracy of fault diagnosis and obtain the global optimal solutions with a higher probability, two strategies, i.e. a hybrid particle position updating strategy and a fixed iteration interval intervention updating strategy, are designed to balance the effect of the local and the global search. These methods increase the diversity of particles, expand the particles ability of searching the entire solution space, and guide the particles adaptively jumping out of the local optimal area. Meanwhile, based on the shorter intra-class distance, longer inter-class distance and maximum classification accuracy of training samples, a fitness function is designed to constraint the output optimal class centers. Experimental results demonstrate that the proposed S-PSO classification algorithm can overcome the problems in the classical clustering algorithms, which only consider the similarity of data instead of their physical meanings. The comparison on GE90 engine borescope image texture feature classification is also conducted. The results show that the performance of S-PSO classification algorithm is robust. Its classification accuracy is higher than those of popular methods, including support vector machine (SVM), neural network, Bayesian classifier, and k-nearest neighbor (k-NN) algorithm.

关键词： Particle swarm optimization classification algorithm hybrid particle position updating strategy fixed iteration interval intervention updating strategy fitness function fault diagnosis

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Greylevel Difference classification algorithm in Fractal Image Compression

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Journal of Computer Science & Technology 2002年第2期17卷 236-240页

作者：陈毅松卢坚孙正兴张福炎 State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210093 P.R. China

This paper proposes the notion of a greylevel difference classification algorithm in fractal image compression. Then an example of the greylevel difference classification algo rithm is given as an improvement of the quadrant greylevel and variance classification in the quadtree-based encoding algorithm. The algorithm incorporates the frequency feature in spatial analysis using the notion of average quadrant greylevel difference, leading to an enhancement in terms of encoding time, PSNR value and compression ratio.

关键词： image coding fractal classification algorithm greylevel differenceclassifica- tion

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Integrating TANBN with cost sensitive classification algorithm for imbalanced data in medical diagnosis

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 140卷 106266-106266页

作者： Gan, Dan Shen, Jiang An, Bang Xu, Man Liu, Na Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Nankai Univ Business Sch Tianjin 300071 Peoples R China

For the imbalanced classification problems, most traditional classification models only focus on searching for an excellent classifier to maximize classification accuracy with the fixed misclassification cost, not take into consideration that misclassification cost can change with sample probability distribution. So far as we know, cost-sensitive learning method can be effectively utilized to solve imbalanced data classification problems. In this regards, we propose an integrated TANBN with cost-sensitive classification algorithm (AdaC-TANBN) to overcome the above drawback and improve classification accuracy. The AdaC-TANBN algorithm employs variable misclassification cost determined by samples distribution probability to train classifier, then implements classification for imbalanced data in medical diagnosis. The effectiveness of our proposed approach is examined on the Cleveland heart dataset (Heart), Indian liver patient dataset (ILPD), Dermatology dataset and Cervical cancer risk factors dataset (CCRF) from the UCI learning repository. The experimental results indicate that the AdaC-TANBN algorithm can outperform other state-of-the-art comparative methods.

关键词： TANBN Cost sensitive Integrated learning classification algorithm Imbalanced data Medical diagnosis

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A classification algorithm Based on Complex Number Feature

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IEEE ACCESS 2020年 8卷 17842-17853页

作者： Xu, Ying Lu, Zefeng Liu, Licai Cai, Daseng Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China

In this study, a classification algorithm based on complex number feature is proposed. Specifically, the SVM framework is reformulated, so each example would be classified in the unitary space. The cost function is redefined by considering the maximum margin of real and imaginary units of the complex number feature at the same time. The cost function is based on the expectation of the hinge loss, and its derivatives can be calculated in closed forms. Using a stochastic gradient descent (SGD) algorithm, this method allows for efficient implementation. For complex number feature, the example uncertainty is modeled by a sample preprocessing method based on within-class Euclidean distance Gaussian distribution sample (DGS). In addition, a complex number feature selection method based on improved hybrid discrimination analysis (HDA) is proposed by considering the correlation between real and imaginary units of complex number feature. The proposed classification algorithm is tested on synthetic data and three publicly available and popular datasets, namely, MNIST, WDBC, and Voc2012. Experimental results verify the effectiveness of the proposed method. The codes are available: https://***/luckysomebody/paper-code

关键词： classification algorithm complex number feature feature fusion feature selection sample uncertainty

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Digital mapping of soil texture classes using Random Forest classification algorithm

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SOIL USE AND MANAGEMENT 2022年第1期38卷 135-149页

作者： Dharumarajan, Subramanian Hegde, Rajendra ICAR Natl Bur Soil Survey & Land Use Planning Reg Ctr Bangalore 560024 Karnataka India

Soil texture is the most important soil physical property that determines water holding capacity, nutrient availability and crop growth. Spatial distribution of soil texture at a higher spatial resolution at regional and national level is essential for crop planning and management. In the present study, we mapped the soil textural classes over 16.2 M ha area of Andhra Pradesh state, India, at 250 m spatial resolution up to 2 m depth using the digital soil mapping approach. A total of 2,272 profile observations were used for the prediction of soil textural classes using the Random Forest (RF) classification algorithm. To estimate textural classes at six standard soil depth intervals (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200 cm), we used continuous depth function of texture distribution using average sand, silt and clay content of different textural classes. Depth-wise spline outputs were then transformed into textural classes as per USDA textural classification. Sixteen environmental variables including Landsat-8 data, digital elevation model attributes and climatic variables were used for modelling. For model building, 75% of data was used and 25% of data was used for validation. Overall classification accuracy index and kappa index were calculated for validation data sets using 100 RF models. We recorded overall accuracy of 50%-65% and kappa index of 35%-47% for various depths. We found that equal-area quadratic splines of average sand, silt and clay are useful for soil profile depth harmonization of soil textural classes and random forest classification algorithm is a promising tool for spatial prediction of texture classes at the regional level. The present high-resolution (250 m) maps of soil texture classes are useful for different hydrological studies and preparation of proper land-use plans.

关键词： classification algorithm digital soil mapping random forest model soil texture

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Knee joint vibration signal classification algorithm based on machine learning

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NEURAL COMPUTING & APPLICATIONS 2021年第3期33卷 985-995页

作者： Zheng, Yi Wang, Youqiang Liu, Jixin Jiang, Haiyan Yue, Qingchao Qingdao Huanghai Univ Inst Intelligence & Mfg Qingdao 266427 Peoples R China Qingdao Univ Technol Sch Mech & Automot Engn Qingdao 266520 Peoples R China Shandong Univ Sci & Technol Dept Mech & Elect Engn Tai An 271000 Shandong Peoples R China

The knee joint is the largest and most complex flexion and extension joint of the human body. It supports most of the weight of the human body during the whole body during standing or exercise. Because the knee joint has the characteristics of complex structure and large load, it is also vulnerable to damage. An effective diagnosis in the early stage of injury or lesion of the knee joint is of great help to the later treatment. At present, the commonly used knee joint examination methods have the problems of large trauma and high cost. Therefore, this paper uses machine learning technology to study the classification algorithm of knee joint vibration signal. The research results of this paper were verified by selecting the subjects to form a healthy group and a disease injury group. The experimental results show that the proposed signal denoising algorithm is superior to the traditional denoising algorithm. After analyzing several classification algorithms, the multi-classifier fusion algorithm has excellent performance in signal classification. The experimental results show that the research results can be applied to the classification of knee joint vibration signals, and then applied to the clinical diagnosis of knee joint diseases.

关键词： Machine learning Knee joint vibration signal (VAG signal) classification algorithm

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Imbalanced data classification algorithm with support vector machine kernel extensions

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EVOLUTIONARY INTELLIGENCE 2019年第3期12卷 341-347页

作者： Wang, Feng Liu, Shaojiang Ni, Weichuan Xu, Zhiming Qiu, Zemin Wan, Zhiping Pan, Zhihong Sun Yat Sen Univ Xinhua Coll Dept Informat Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Xinhua Coll Dept Equipment & Lab Management Guangzhou Guangdong Peoples R China

Learning from the imbalanced data samples so as to achieve accurate classification is an important research content in data mining field. It is very difficult for classification algorithm to achieve a higher accuracy because the uneven distribution of data samples makes some categories have few samples. A imbalanced data classification algorithm of support vector machines (KE-SVM) is proposed in this article, this algorithm achieve the initial classification of data samples by training the maximum margin classification SVM model, and then obtaining a new kernel extension function. based on Chi square test and weight coefficient calculation, through training the samples again by the new vector machine with kernel function to improve the classification accuracy. Through the simulation experiments of real data sets of artificial data set, it shows that the proposed method has higher classification accuracy and faster convergence for the uneven distribution data.

关键词： Support vector machine classification algorithm Imbalanced data Kernel extension function

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Developed ABCLASS-Miner classification algorithm Based Rule Extraction for Denim Fabrics

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2024年第1期37卷 326-337页

作者： Katircioglu, Gozde Aydogan, Emel Kizilkaya Akgul, Esra Delice, Yilmaz Erciyes Univ Fac Engn Dept Ind Engn Kayseri Turkiye Erciyes Univ Fac Engn Dept Ind Design Engn Kayseri Turkiye Kayseri Univ Fac Appl Sci Dept Int Trade & Logist Kayseri Turkiye

Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become useful only when they are analyzed for a specific reason. Data mining may transform raw data into useful information. In the present study, classification and analysis of denim fabric quality characteristics according to denim fabric production parameters were carried out. The present study proposes a new classification rule inference algorithm. The suggested approach is mostly based on Artificial Bee Colony Optimization (ABC), a swarm intelligence meta-heuristic. In each step of the algorithm, there are two phases called the employed bee phase and the onlooker bee phase. This algorithm has been compared with the classification algorithms in the related literature. This proposed algorithm is a new data mining tool that intelligently combines various metaheuristic and neural networks and can generate classification rules. The results indicate that the proposed data mining algorithms may be highly useful in determining weight and width in denim fabric manufacture.

关键词： Data mining Artificial bee colony classification algorithm Denim fabrics

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A Parallel and Reverse Learn&x002B;&x002B;.NSE classification algorithm

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IEEE ACCESS 2020年 8卷 64157-64168页

作者： Shen, Yan Du, Jianguo Tong, Jingen Dou, Qian Jing, Luyi Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China China Natl Heavy Duty Truck Grp Co Ltd Jinan 250000 Peoples R China Jiangsu Univ Sch Management Zhenjiang 212013 Jiangsu Peoples R China

There are lots of typical applications of classification learning for accumulated big data in the nonstationary environments. It is very necessary and urgent to study the algorithms that can carry out classification learning efficiently in these environments. The recently proposed algorithm, named Learn & x002B;& x002B;.NSE, has made an important breakthrough, which is one of the important research achievements in this research field. However, the Learn & x002B;& x002B;.NSE algorithm adopts a serial ensemble mechanism, and its execution efficiency needs to be further improved when facing the long-term accumulated big data. A Parallel and Reverse Learn & x002B;& x002B;.NSE algorithm, abbreviated as PRLearn & x002B;& x002B;.NSE, is proposed in this paper by changing the ensemble mechanism of the base-classifiers, which uses the old base-classifiers as a supplement to the new base-classifier. It constructs a fast and parallel ensemble mechanism. The experimental results on the artificially generated dataset and real dataset show that the proposed PRLearn & x002B;& x002B;.NSE algorithm can greatly improve the efficiency of ensemble classification learning under the premise of obtaining the approaching classification accuracy of Learn & x002B;& x002B;.NSE algorithm and it is very suitable for fast ensemble classification learning for the long-term accumulated big data.

关键词： classification algorithm big data mining ensemble learning nonstationary environment

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A real-time classification algorithm for EEG-based BCI driven by self-induced emotions

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015年第3期122卷 293-303页

作者： Iacoviello, Daniela Petracca, Andrea Spezialetti, Matteo Placidi, Giuseppe Univ Roma La Sapienza Dept Comp Control & Management Engn Antonio Ruber I-00185 Rome Italy Univ LAquila Dept Life Hlth & Environm Sci A2VI Lab I-67100 Laquila Italy

Background and objective: The aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. Method: The peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. Results: Some experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the

关键词： Affective computing BCI classification algorithm EEG signals Principal components analysis Self-induced emotions

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