International Journal of Computational Intelligence Systems

Volume 8, Issue 3, June 2015
Sara del Río, Victoria López, José Manuel Benítez, Francisco Herrera
Pages: 422 - 437
The big data term is used to describe the exponential data growth that has recently occurred and represents an immense challenge for traditional learning techniques. To deal with big data classification problems we propose the Chi-FRBCS-BigData algorithm, a linguistic fuzzy rule-based classification...
Boris Yatsalo, Vladimir Didenko, Sergey Gritsyuk, Terry Sullivan
Pages: 467 - 489
A new framework, , for multicriteria decision analysis (MCDA) of a wide range of practical problems on risk management is introduced. framework contains a library of modules that are the basis for two scalable systems: for analysis of multicriteria problems, and for multicriteria analysis of spatial...
Fangzhou Liu, Ting Wang, Sheng-Uei Guan, Ka Lok Man
Pages: 490 - 501
Incremental Attribute Learning (IAL) is a feasible approach for solving high-dimensional pattern recognition problems. It gradually trains features one by one. Previous research indicated that supervised machine learning with input attribute ordering can improve classification results. Moreover, input...
Fujin Zhu, Xuefeng Wang, Donghua Zhu, Yuqin Liu
Pages: 502 - 516
Patent classification systems are applied extensively in innovative analysis. Existing patent classification schemes are either technology-dependent or TRIZ-based. The former ones, such as the IPC and UPC, are normally developed by different patent offices in the world mainly for the purpose of patentability...
Ch. Sanjeev Kumar Dash, Satchidananda Dehuri, Sung-Bae Cho, Gi-Nam Wang
Pages: 539 - 552
This work presents an accurate and smooth functional link artificial neural network (FLANN) for classification of noisy database. The accuracy and smoothness of the network is taken birth by suitably tuning the parameters of FLANN using differential evolution and filter based feature selection. We use...
Fanyong Meng, Chunqiao Tan, Xiaohong Chen
Pages: 591 - 605
Prospect theory is a very effective method to express behavioral decision making under uncertainty. This paper attempts to develop a method to multi-attribute decision making with Atanassov's interval-valued intuitionistic fuzzy information using prospect theory. This method first transforms Atanassov's...