editorial artificial intelligence and data mining...

EditorialArtificial Intelligence and Data Mining 2014

Fuding Xie,1 Suohai Fan,2 Jianzhou Wang,3 Helen Lu,4 and Caihong Li5

1 School of Urban and Environmental Sciences, Liaoning Normal University, Dalian 116029, China2 School of Information Science and Technology, Jinan University, Guangzhou 510632, China3 School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China4Department of Software Engineering, University of Technology, Sydney, NSW 2007, Australia5 School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

Correspondence should be addressed to Fuding Xie; [email protected]

Received 21 August 2014; Accepted 21 August 2014; Published 14 October 2014

Copyright © 2014 Fuding Xie et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Artificial intelligence and data mining techniques have beenwidely used in many domains to solve classification, plan-ning, diagnosis, computation, prediction, and optimizationproblems. The aim of this special issue is to reflect the latestdevelopment in this research field and provide advancedknowledge for researchers actively working on algorithmsand applications of artificial intelligence.The accepted papersin this issue are concerned with the following topics: (i)forecasting models based on statistical methods and artificialintelligence; (ii) advanced artificial intelligence algorithm andnovel datamining techniques; (iii) computational intelligencein medical science and biology; (iv) time series analysis ineconomics and finance; (v) machine learning on massivedatasets.

Among them, there are nine papers regarding forecast-ing models based on artificial intelligence and data min-ing techniques. In “Short-term wind speed forecasting usingdecomposition-based neural networks combining abnormaldetection method,” authored by X. Chen et al., two three-stage hybrid approaches are developed for short-term windspeed forecasting. In “Wind power assessment based on aWRF wind simulation with developed power curve modelingmethods,” authored by Z. Guo and X. Xiao, the authorspropose two improved power curve modeling methods. In“A hybrid approach by integrating brain storm optimizationalgorithmwith grey neural network for stock index forecasting,”authored by Y. Sun, a novel hybrid model based on thebrain storm optimization approach is constructed for stockindex forecast. In “Hybrid wind speed forecasting model study

based on SSA and intelligent optimized algorithm,” authoredby W. Zhang et al., the authors investigate singular spectrumanalysis in three time series models to forecast wind speed.In “Intelligent optimized combined model based on GARCHand SVM for forecasting electricity price of New South Wales,Australia,” authored by Y. Yang et al., the researchers suggestan optimized combined forecasting model by ant colonyoptimization algorithm. In “A hybrid forecasting model basedon bivariate division and a backpropagation artificial neuralnetwork optimized by chaos particle swarm optimization forday-ahead electricity price,” authored by Z. Wang et al., abivariate division backpropagation artificial neural networkmethod based on chaos particle swarm optimization isutilized for electricity price prediction. In “An optimizedforecasting approach based on grey theory and cuckoo searchalgorithm: a case study for electricity consumption in NewSouth Wales,” authored by P. Jiang et al., the authors builda hybrid grey model optimized by cuckoo search algo-rithm. In “Radial basis function neural network based onan improved exponential decreasing inertia weight-particleswarm optimization algorithm for AQI prediction,” authoredby J. Lu et al., a novel radial basis function neural networkmodel optimized by improved particle swarm optimizationis developed. In “Deterministic echo state networks based stockprice forecasting,” authored by J. Dan et al., echo state networkwith deterministically constructed reservoir is investigated.

Second, there are six papers on new algorithm and newmodel design. In “Online learning discriminative dictionarywith label information for robust object tracking,” authored by

Hindawi Publishing CorporationAbstract and Applied AnalysisVolume 2014, Article ID 819641, 2 pageshttp://dx.doi.org/10.1155/2014/819641

2 Abstract and Applied Analysis

B. Fan et al., a supervised approach to online learn a struc-tured sparse and discriminative representation for objecttracking is proposed. In “Approximate accuracy approaches toattribute reduction for information systems,” authored by D.Liu et al., a new approach to attribute reduction for incom-plete and fuzzy valued information systems is proposed. In“Moment conditions selection based on adaptive penalizedempirical likelihood,” authored by Y. Song, an empiricallikelihood shrinkagemethod is developed to estimate param-eters and select correct moment conditions. In “A hybridsampling SVM approach to imbalanced data classification,”authored by Q. Wang, a hybrid sampling SVM approachis proposed combining oversampling and undersamplingtechniques. In “Cost-sensitive support vector machine usingrandomized dual coordinate descent method for big class-imbalanced data classification,” authored by M. Tang et al.,a cost-sensitive support vector machine using randomizeddual coordinate descent method is proposed. In “Researchon adaptive optics image restoration algorithm by improvedexpectation maximization method,” authored by L. Zhanget al., the authors put forward a deconvolution algorithmimproved by the expectation maximization algorithm.

Some papers collected in this special issue are concernedwith some applications to medical science and biology. In“Evaluating the risk of metabolic syndrome based on an arti-ficial intelligence model,” authored by H. Chen et al., a modelis developed to identify adults at risk of metabolic syndromebased on physical signs. In “High-risk immunization strategiesfor multiethnic regions,” authored by F. Nian and T. Liu, somehigh-risk immunization strategies formultiethnic regions areproposed. In “Identification of protein coding regions in theeukaryotic DNA sequences based on Marple algorithm andwavelet packets transform,” authored by G. Liu and Y. Luan,a novel algorithm based on autoregressive spectrum analysisand wavelet packets transform is presented.

Several authors are also interested in the applications toeconomics and finance. In “Adaptive heterogeneous autore-gressive models of realized volatility based on a genetic algo-rithm,” authored by H. Qu and P. Ji, the authors propose anadaptive heterogeneous autoregressive model. In “Dynamiccommunities in stock market,” authored by X. Gui et al.,the authors introduce an approach to construct stock net-works based on stock prices. In “A multitarget land usechange simulation model based on cellular automata and itsapplication,” authored by J. Yang et al., the authors proposea multitarget land use change simulation model based oncellular automata.

Finally, other applied problems are considered on differ-ent domains. In “Comprehensive influence model of preschoolchildren’s personality development based on the Bayesian net-work,” authored by Y. Sun et al., an influencing factor modelof personality development based on the Bayesian network isproposed. In “Identifying the best coach by an improved AHPmodel,” authored by J. Xing et al., a model is proposed toevaluate college coaches of a century. In “A weighted votingclassifier based on differential evolution,” authored byY. Zhanget al., the authors propose a weighted voting approach basedon differential evolution. In “The influence of traffic networkson the supply-demand balance of tourism: a case study of

Jiangsu province, China,” authored byT.Chen, a road networkis established to address the impact of traffic networks on thesupply-demand balance of tourism. In “Recognition of processdisturbances for an SPC/EPC stochastic system using supportvector machine and artificial neural network approaches,”authored by Y. E. Shao, the author proposes the integrationof support vector machine and artificial neural networkapproaches to recognize the disturbance patterns. In “Targetimage matching algorithm based on binocular CCD ranging,”authored by D. Li et al., the authors propose target imagein a subpixel level matching algorithm for binocular CCDranging. In “A solution to reconstruct cross-cut shreddedtext documents based on character recognition and geneticalgorithm,” authored by H. Xu et al., a feature-matchingalgorithm based on the character recognition via establishingthe database of the letters is presented.

Acknowledgments

The guest editors of this special issue would like to expresstheir thanks to the authors who have submitted papers forconsideration and the referees of the submitted papers.

Fuding XieSuohai Fan

Jianzhou WangHelen Lu

Caihong Li

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