Incorporating an EM-Approach for Handling Missing Attribute-Values in Decision Tree Induction

Authors:
Amitava Karmaker
Affiliations:
University of Texas at San Antonio
Venue:
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Year:
2005

Citing 0
Cited 3

On the influence of imputation in classification: practical issues

Journal of Experimental & Theoretical Artificial Intelligence
Missing Data Analysis: A Kernel-Based Multi-Imputation Approach

Transactions on Computational Science III
Information enhancement for data mining

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

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Abstract

Data with missing attribute-values are quite common in many classification problems. In this paper, we incorporate an Expectation-Maximization(EM) inspired approach for filling up missing values to decision tree learning with the objective of improving classification accuracy. Here, each missing attribute-value is iteratively filled using a predictor constructed from the known values and predicted values of the missing attribute-values from the previous iteration. We show that our approach significantly outperforms some standard machine learning methods for handling missing values in classification tasks.