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Article

AN INTELLIGENT FEATURE SELECTION AND CLASSIFICATION METHOD BASED ON HYBRID ABC–SVM

This paper presents a new approach to feature selection and classifcation based on support vector machine and hybrid artificial bee colony. The approach consists of two stages. At the first stage, this paper presented a hybrid artificial bee colony-based classifier model that combines artificial bee colony to improve classification accuracy with the most superior model parameter and features were selected from the original feature set. The classification accuracy and the feature subset provided

Jie Li, Qiuwen Zhang, Zhang Yongzhi, Li Chang, Xiao Jian

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1876–

Research paper

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems , ISSUE 2, 448–465

Article

AN INVESTIGATION OF DECISION ANALYTIC METHODOLOGIES FOR STRESS IDENTIFICATION

In modern society, more and more people are suffering from some type of stress. Monitoring and timely detecting of stress level will be very valuable for the person to take counter measures. In this paper, we investigate the use of decision analytics methodologies to detect stress. We present a new feature selection method based on the principal component analysis (PCA), compare three feature selection methods, and evaluate five information fusion methods for stress detection. A driving stress

Yong Deng, Chao-Hsien Chu, Huayou Si, Qixun Zhang, Zhonghai Wu

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1675–1699

Article

FEATURE SELECTION ALGORITHM BASED ON CONDITIONAL DYNAMIC MUTUAL INFORMATION

Aim at existing selection algorithm mutual information inaccurate valuation problem, a condition dynamic concept of mutual information. On this basis, the conditions proposed based on dynamic mutual information (CDMI) feature selection algorithm to overcome the traditional mutual information selection process dynamic correlation problem; conditions of dynamic mutual information throughout the selection process is dynamic valuation, those the samples can be identified after each selection

Wang Liping

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 316–337

Article

HETEROSCEDASTIC DISCRIMINANT ANALYSIS COMBINED WITH FEATURE SELECTION FOR CREDIT SCORING

Discriminant Analysis (Fisher, 1936, Krzyśko, 1990) and a feature selection algorithm that retains sufficient information for classification purpose. We have tested five feature subset selection algorithms: two filters and three wrappers. To evaluate the accuracy of the proposed credit scoring model and to compare it with the existing approaches we have used the German credit data set from the study (Chen, Li, 2010). The results of our study suggest that the proposed hybrid approach is an effective and

Katarzyna Stąpor, Tomasz Smolarczyk, Piotr Fabian

Statistics in Transition New Series , ISSUE 2, 265–280

Article

LOCALLY REGULARIZED LINEAR REGRESSION IN THE VALUATION OF REAL ESTATE

advantageous to build local regression models than a global model. Additionally, we propose a local feature selection via regularization. The empirical research carried out on three data sets from real estate market confirmed the effectiveness of this approach. We paid special attention to the model quality assessment using cross-validation for estimation of the residual standard error.

Mariusz Kubus

Statistics in Transition New Series , ISSUE 3, 515–524

Article

Smartphone Application for Fault Recognition

1Nishchal K. Verma, Rahul K. Sevakula, Jayesh K. Gupta, Sumanik Singh, Sonal Dixit, Al Salour

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1763–1782

Article

Learning Better Classification-based Reordering Model for Phrase-based Translation

Li Fuxue, Xiao Tong, Zhu Jingbo

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 145–152

Article

EMPIRICAL MODE DECOMPOSITION AND ROUGH SET ATTRIBUTE REDUCTION FOR ULTRASONIC FLAW SIGNAL CLASSIFICATION

attribute reduction (RSAR) can be applied to implement feature selection. Finally, the selected features are taken as input of artificial neural networks (ANNs) to train the decision classifier for flaw identification. Experimental results show that compared to conventional wavelet transform based schemes and principal components analysis, EMD combined with RSAR can improve the performance of feature extraction and selection. Using such hybrid scheme can effectively classify different ultrasonic flaw

Yu Wang

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1401–1420

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