C4.5: programs for machine learning
C4.5: programs for machine learning
Data preparation for data mining
Data preparation for data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Cluster-Based Algorithms for Dealing with Missing Values
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Possibilistic pattern recognition in a digestive database for mining imperfect data
WSEAS TRANSACTIONS on SYSTEMS
Inferring a possibility distribution from empirical data
Fuzzy Sets and Systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Using multiple imputation to simulate time series: a proposal to solve the distance effect
WSEAS Transactions on Computers
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An approach that deals with the heterogeneity and the imperfection of information elements which constitute the objects in large databases has been proposed in this paper. Unlike the prior works that separately tackle these aspects using complex and conditional techniques, our method is general and takes account of them within a simple, flexible, and robust unified framework. It is fundamentally based on two fuzzy monotone measures: the possibility and the necessity degrees introduced in the theory of possibilities. A simple concrete example will also be given to clarify and to simply illustrate the main steps of computation, pointing out the outperformance and the robustness of the proposed strategy.