scholarly journals Computational Intelligence and Data Mining in Sports

2021 ◽  
Vol 11 (6) ◽  
pp. 2637
Author(s):  
Iztok Fister ◽  
Iztok Fister
Keyword(s):  
Data Mining ◽  
Computational Intelligence

Sport can be viewed from two standpoints: professional and recreational [...]

Hybrid Wavelet-Neuro-Fuzzy Systems of Computational Intelligence in Data Mining Tasks

2017 ◽  
pp. 787-825
Author(s):  
Yevgeniy Bodyanskiy ◽  
Olena Vynokurova ◽  
Oleksii Tyshchenko
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Process ◽  
Computational Intelligence ◽  
Group Method ◽  
Learning Criterion ◽  
Adjustment Procedure ◽  
Artificial Neural ◽  
Input Variables

This work is devoted to synthesis of adaptive hybrid systems based on the Computational Intelligence (CI) methods (especially artificial neural networks (ANNs)) and the Group Method of Data Handling (GMDH) ideas to get new qualitative results in Data Mining, Intelligent Control and other scientific areas. The GMDH-artificial neural networks (GMDH-ANNs) are currently well-known. Their nodes are two-input N-Adalines. On the other hand, these ANNs can require a considerable number of hidden layers for a necessary approximation quality. Introduced Q-neurons can provide a higher quality using the quadratic approximation. Their main advantage is a high learning rate. Universal approximating properties of the GMDH-ANNs can be achieved with the help of compartmental R-neurons representing a two-input RBFN with the grid partitioning of the input variables' space. An adjustment procedure of synaptic weights as well as both centers and receptive fields is provided. At the same time, Epanechnikov kernels (their derivatives are linear to adjusted parameters) can be used instead of conventional Gauss functions in order to increase a learning process rate. More complex tasks deal with stochastic time series processing. This kind of tasks can be solved with the help of the introduced adaptive W-neurons (wavelets). Learning algorithms are characterized by both tracking and smoothing properties based on the quadratic learning criterion. Robust algorithms which eliminate an influence of abnormal outliers on the learning process are introduced too. Theoretical results are illustrated by multiple experiments that confirm the proposed approach's effectiveness.

Cat Swarm Optimization Supported Data Mining

2010 ◽  
pp. 26-43
Author(s):  
Pei-Wei Tsai ◽  
Jeng-Shyang Pan ◽  
Bin-Yih Liao ◽  
Shu-Chuan Chu ◽  
Mei-Chiao Lai
Keyword(s):  
Data Mining ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Swarm Optimization ◽  
Cat Swarm Optimization

This chapter reviews the basic idea and processes in data mining and some algorithms within the field of evolutionary computing. The authors focus on introducing the algorithms of computational intelligence since they are useful tools for solving problems of optimization, data mining, and many kinds of industrial issues. A feasible model of combining computational intelligence with data mining is presented at the end of the chapter with the conclusions.

Data mining based on computational intelligence

2005 ◽  
Vol 10 (2) ◽  
pp. 371-374 ◽  
Author(s):  
Wang Yuan-zhen ◽  
Zhang Zhi-bing ◽  
Yi Bao-lin ◽  
Li Hua-yang
Keyword(s):  
Data Mining ◽  
Computational Intelligence

Computational intelligence techniques for biological data mining: An overview

2014 ◽  
Author(s):  
Ibrahima Faye ◽  
Muhammad Javed Iqbal ◽  
Abas Md Said ◽  
Brahim Belhaouari Samir
Keyword(s):  
Data Mining ◽  
Computational Intelligence ◽  
Biological Data
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