Statistical analysis with missing data
Statistical analysis with missing data
Collective computation in neuronlike circuits
Scientific American
Imputation techniques in regression analysis: looking closely at their implementation
Computational Statistics & Data Analysis
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
On semantic issues connected with incomplete information databases
ACM Transactions on Database Systems (TODS)
On Databases with Incomplete Information
Journal of the ACM (JACM)
Incomplete Information: Rough Set Analysis
Incomplete Information: Rough Set Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Computers and Operations Research
Classification with incomplete survey data: a Hopfield neural network approach
Computers and Operations Research
Computers and Operations Research
Test-Cost Sensitive Classification on Data with Missing Values
IEEE Transactions on Knowledge and Data Engineering
Effects of the neural network s-sigmoid function on KDD in the presence of imprecise data
Computers and Operations Research
Multiple criteria classification with an application in water resources planning
Computers and Operations Research
The disposal of incomplete classification data in teaching evaluation system
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
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Data for classification are often incomplete. The multiple-values construction method (MVCM) can be used to include data with missing values for classification. In this study, the MVCM is implemented by using fuzzy sets theory in the context of classification with discrete data. By using the fuzzy sets based MVCM, data with missing values can add values to classification, but can also introduce excessive uncertainty. Furthermore, the computational cost for the use of incomplete data could be prohibitive if the scale of missing values is large. This paper discusses the association between classification performance and the use of incomplete data. It proposes an algorithm of near-optimal use of incomplete classification data. An experiment with real-world data demonstrates the usefulness of the algorithm.