A Framework for Prediction Using Rough Set and Real Coded Genetic Algorithm

被引：0
作者：
R. Rathi
D. P. Acharjya
机构：
[1] VIT University,School of Information Technology and Engineering
[2] VIT University,School of Computer Science and Engineering
来源：
Arabian Journal for Science and Engineering | 2018年 / 43卷
关键词：
Rough set; Reduct; Indiscernibility; Genetic algorithm; Prediction; Linear regression;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Internet made a drastic change in the way data are collected. There has been huge and huge collections of data. All these data serve no purpose unless some useful information is mined from it. Prediction of future instances will be major research problem. In this work, we adapt a rough and real coded genetic algorithm-based prediction system for prediction of future instances. We adapt rough set in this work because of uncertainties present in the data. Additionally, it is used to eliminate the unwanted attributes. Real coded genetic algorithm is used to predict the values for the unknown instances by making use of multiple linear regression. The model is experimented over agriculture data obtained from Tiruvannamalai district of Tamil Nadu. The experimental results show the viability of proposed research.
引用
收藏
页码：4215 / 4227
页数：12
相关论文
共 42 条
[1]  
Ray BK(1993)Long-range forecasting of IBM product revenues using a seasonal fractionally differenced ARMA model Int. J. Forecast. 9 255-269
[2]  
Khashei M(2011)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting Appl. Soft Comput. 11 2664-2675
[3]  
Bijari M(2012)Prediction of missing associations using rough computing and Bayesian classification Int. J. Intell. Syst. Appl. 4 1-808
[4]  
Acharjya DP(2010)Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting Knowl. Based Syst. 23 800-1629
[5]  
Roy D(2010)A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting Inf. Sci. 180 1610-524
[6]  
Rahaman MA(2014)Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease BMC Bioinform. 15 S11-2757
[7]  
Hadavandi E(2015)Neural network and rough set hybrid scheme for prediction of missing associations Int. J. Bioinform. Res. Appl. 11 503-356
[8]  
Shavandi H(2011)Colon cancer prediction with genetics profiles using evolutionary techniques Expert Syst. Appl. 38 2752-206
[9]  
Ghanbari A(1982)Rough sets Int. J. Parallel Program. 11 341-51
[10]  
Cheng CH(2014)A weighted multiple regression model to predict rainfall patterns: principal component analysis approach Mediterr. J. Soc. Sci. 5 34-230
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