Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 5
1400-1405

Conditional-probability zone transformation coding for categorical features

He Liang1
Xu Zhengguo1
Li Yun1
Shen Chao2
1. National Key Laboratory of Science & Technology on Blind Signal Processing, Chengdu 610041, China
2. MOE Key Laboratory for Intelligent Networks & Network Security, Xi'an Jiaotong University, Xi'an 710049, China
DOI: 10.19734/j.issn.1001-3695.2018.10.0818

Abstract

Categorical features always exist in the dataset and coding them is a key issue for solving problems efficiently by machine learning models. One-hot coding is a wide accepted method to convert the features into feature values, and however it attracted sparse space and meaningless value after coding. To improve the coding performance, this paper designed a novel co-ding method based on conditional probability after dividing the features into zones, which was called CZT coding. The CZT coding calculated the conditional probability of each feature and then divided the features into several zones and finally coded the features in each zone. It mathematically proved that compared with the state-of-the-art method—one-hot coding, CZT co-ding reduced the code length by at least the mean of feature spaces and the issue switched into an easier one after CZT coding for the following machine learning model. Finally, it used the same neuron network as the classifier, the performance of CZT coding and one-hot coding was compared by using the Titanic dataset. The result shows that CZT coding makes the classifier performs better both on the accuracy and steadiness.

Foundation Support

国家自然科学基金重点资助项目(U1736205)
国家自然科学基金资助项目(61773310)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.10.0818
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 5
Section: Algorithm Research & Explore
Pages: 1400-1405
Serial Number: 1001-3695(2020)05-023-1400-06

Publish History

[2020-05-05] Printed Article

Cite This Article

贺亮, 徐正国, 李赟, 等. 非数值化特征的条件概率区域划分(CZT)编码方法 [J]. 计算机应用研究, 2020, 37 (5): 1400-1405. (He Liang, Xu Zhengguo, Li Yun, et al. Conditional-probability zone transformation coding for categorical features [J]. Application Research of Computers, 2020, 37 (5): 1400-1405. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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