Classification Algorithm Based on Categorical Data Analysis

Yu Sheng Cheng; Xiang Li; Mei Wen Ding

doi:10.4028/www.scientific.net/AMM.333-335.1292

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 333-335Classification Algorithm Based on Categorical Data...

Classification Algorithm Based on Categorical Data Analysis

Abstract:

Through compares three methods of Standard Deviation mathematical expectation and variance, a classification algorithm based on the Standard Deviation which in the training set is proposed in this paper. The algorithm first mapped the discrete attribute values to the corresponding values, and calculates Standard Deviation, mathematical expectation and Variance of each attribute in each category. The Standard Deviation, mathematical expectation and Variance of each attribute in each category used as coordinates. When there are new datas need to determine the category, we just need to use the attributes of the new data as coordinates, and calculate its distance to each category, and then the data type is the shortest distance category. Comparison of three methods, the Standard Deviation is the most stable and most accurate. This algorithm has advantages in dealing with the noisy date.

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Periodical:

Applied Mechanics and Materials (Volumes 333-335)

Pages:

1292-1295

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.333-335.1292

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Online since:

July 2013

Authors:

Yu Sheng Cheng, Xiang Li, Mei Wen Ding

Keywords:

Categorical Data Analysis, Data Classification, Standard Deviation

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References

[1] Alan Agresti. Attribute data analysis[M], Beijing: Higher Education Press (In Chinese).

Google Scholar

[2] Xiujun Gong, Jianping Sun, Zhongzhi Shi, Active Bayesian network classifiers[J] Computer research and development, July 2002 (5) : 574-579 (In Chinese).

Google Scholar

[3] Lihua Luan, Genlin Ji. Decision tree classification technology research [J]. Computer engineering, 2004, 21 (9) : 94-96. (In Chinese).

Google Scholar

[4] Yusheng Cheng, Chunmei Zhang, Xuegang Hu. An incremental inductive learning algorithm [J]. Journal of chongqing university (natural science edition), 2004, 27(4): 44 -48. (In Chinese).

Google Scholar

[5] Zhang Lin, Zhang Jianli. Based on the class attribute mathematical expectation classification algorithm [J], anqing normal university (natural science edition), 2003, 1: 31-34. (In Chinese).

Google Scholar