Missing Value Imputation and an Attempt Toward Machine Learning by Rule Generation in Rough Set Non-Deterministic Information Analysis

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  • SAKAI Hiroshi
    Faculty of Management and Information Sciences, Josai International University
  • NAKATA Michinori
    Faculty of Management and Information Sciences, Josai International University

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  • ラフ集合非決定情報解析における欠損値補完とルール生成による機械学習に向けた試み

Abstract

<p>Rough Set Non-deterministic Information Analysis (RNIA) is a mathematical framework for analyzing categorical tabular data and can be considered as a framework that adds information incompleteness to rough set theory. In RNIA, the DIS-Apriori method generates rules from the regular table Deterministic Information System (DIS)， and the NIS-Apriori method generates certain rules and possible rules from the table Non-deterministic Information System (NIS) with missing values or non-deterministic information.</p><p>In this paper, we add two new features, missing value imputation and machine learning, to RNIA. It is usually considered difficult to generate rules from table data sets containing many missing values, however, we can generate certainty rules from such tables. Using this property, we generate certain rules with an attribute including missing values as the decision attribute and apply them to impute missing values. We term this procedure Self-obtained Rule-based Imputation procedure (SRI method) for imputing missing values. This method is unsupervised learning and does not require background knowledge or additional information. In addition, if the SRI method is applied to each attribute including missing values, it is possible to realize the functionality of self-learning DIS sequentially from NIS. We term this framework Machine Learning by Rule Generation (MLRG). Through some experiments, we clarify the SRI method’s validity and consider the possibility of MLRG framework.</p>

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