Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
On Fusers that Perform Better than Best Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Success and Failure Factors in Software Reuse
IEEE Transactions on Software Engineering
Machine Learning
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
More Success and Failure Factors in Software Reuse
IEEE Transactions on Software Engineering
Comments on "More Success and Failure Factors in Software Reuse"
IEEE Transactions on Software Engineering
Extracting symbolic rules from trained neural network ensembles
AI Communications - Special issue on Artificial intelligence advances in China
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IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
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Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble
IEEE Transactions on Information Technology in Biomedicine
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A serious problem encountered by machine learning and data mining techniques in software engineering is the lack of sufficient data. For example, there are only 24 examples in the current largest data set on software reuse. In this paper, a recently proposed machine learning algorithm is modified for mining extremely small data sets. This algorithm works in a twice-learning style. In detail, a random forest is trained from the original data set at first. Then, virtual examples are generated from the random forest and used to train a single decision tree. In contrast to the numerous discrepancies between the empirical data and expert opinions reported by previous research, our mining practice shows that the empirical data are actually consistent with expert opinions. Copyright © 2008 John Wiley & Sons, Ltd.