Computational learning theory: an introduction
Computational learning theory: an introduction
Learning decision tree classifiers
ACM Computing Surveys (CSUR)
Principle of information diffusion
Fuzzy Sets and Systems
Rough Set Based Data Exploration Using ROSE System
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
A comparative assessment of classification methods
Decision Support Systems
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
A frequency assessment expert system of piezoelectric transducers in paucity of data
Expert Systems with Applications: An International Journal
Data attribute reduction using binary conversion
WSEAS Transactions on Computers
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Many studies about learning in limited data were made in recent years. Without double, small data set learning is a challenging problem. Information in data of small size is scarce and has some learning limit. While discussing the learning accuracy in limited data, different classification method causes different results for different data because each classification method has its property. A method is the best solution for one data but is not the best for another. Therefore, this study analyzes the characteristics of small data set learning by the comparison of classification methods. The Mega-fuzzification method for small data set learning is applied mainly. The comparison of different classification methods for small data set learning with several kinds of data is also presented.