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Mining incomplete survey data through classification

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Abstract

Data mining with incomplete survey data is an immature subject area. Mining a database with incomplete data, the patterns of missing data as well as the potential implication of these missing data constitute valuable knowledge. This paper presents the conceptual foundations of data mining with incomplete data through classification which is relevant to a specific decision making problem. The proposed technique generally supposes that incomplete data and complete data may come from different sub-populations. The major objective of the proposed technique is to detect the interesting patterns of data missing behavior that are relevant to a specific decision making, instead of estimation of individual missing value. Using this technique, a set of complete data is used to acquire a near-optimal classifier. This classifier provides the prediction reference information for analyzing the incomplete data. The data missing behavior concealed in the missing data is then revealed. Using a real-world survey data set, the paper demonstrates the usefulness of this technique.

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Abbreviations

BPNN:

Layered back-propagation neural networks

CCR:

Correct classification rate

HMDA:

Home Mortgage Disclosure Act

LDA:

Linear discriminant analysis

MI:

Multiple imputation

MSA:

Metropolitan Statistical Area

C :

Classifier

RC :

A set of reference information

RM :

A set of classification results for SM k

S :

A set of survey data with incomplete data

SC :

A data set with complete data

SC Test :

Sub-set of SC for test of the classifier

SC Train :

Sub-set of SC for training of the classifier

SM :

A set of survey data with missing values

SM k :

Data sets with artificial imputation values for the missing values

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Authors and Affiliations

  1. Sobey School of Business, Saint Mary’s University, Halifax, NS, B3H 2W3, Canada

    Hai Wang

  2. Charlton College of Business, University of Massachusetts Dartmouth, Dartmouth, MA, 02747-2300, USA

    Shouhong Wang

Authors
  1. Hai Wang
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  2. Shouhong Wang
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Correspondence to Shouhong Wang.

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Wang, H., Wang, S. Mining incomplete survey data through classification. Knowl Inf Syst 24, 221–233 (2010). https://doi.org/10.1007/s10115-009-0245-8

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