Automatic data mining by asynchronous parallel evolutionary algorithms

2002 ◽  
Author(s):  
Jiandong Li ◽  
Zhuo Kang ◽  
Yan Li ◽  
Hongqing Cao ◽  
Pu Liu
Keyword(s):  
Data Mining ◽  
Evolutionary Algorithms ◽  
Automatic Data ◽  
Parallel Evolutionary Algorithms ◽  
Asynchronous Parallel

Asynchronous parallel evolutionary algorithms for constrained optimizations

2000 ◽  
Vol 5 (4) ◽  
pp. 406-412
Author(s):  
Kang Li-shan ◽  
Liu Pu ◽  
Kang Zhuo ◽  
Li Yan ◽  
Chen Yu-ping
Keyword(s):  
Evolutionary Algorithms ◽  
Parallel Evolutionary Algorithms ◽  
Asynchronous Parallel

A UMDA-Based Discretization Method for Continuous Attributes

2011 ◽  
Vol 403-408 ◽  
pp. 1834-1838
Author(s):  
Jing Zhao ◽  
Chong Zhao Han ◽  
Bin Wei ◽  
De Qiang Han
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Evolutionary Algorithms ◽  
Marginal Distribution ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Experimental Results ◽  
Discretization Method ◽  
Bottom Up ◽  
Global Dynamic

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univariate Marginal Distribution Algorithm is a modified Evolutionary Algorithms, which has some advantages over classical Evolutionary Algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of Univariate Marginal Distribution Algorithm.The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

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