C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating concept difficulty with cross entropy
Knowledge discovery and data mining
Machine Learning
A Mathematical Theory of Communication
A Mathematical Theory of Communication
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Generating a set of rules to determine the gender of a speaker of a Japanese sentence
WSEAS TRANSACTIONS on COMMUNICATIONS
Classification of wetland from TM imageries based on decision tree
WSEAS Transactions on Information Science and Applications
Classification of wetland from TM imageries based on decision tree
WSEAS Transactions on Information Science and Applications
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The main focus of data mining is to present hidden knowledge located in large amount of data in human understandable form. Therefore the knowledge representation has to be simple and easy to interpret, possibly without the computer. Decision trees are one of the most transparent methods often used in data mining, but can we make them user friendly? In the process of decision tree induction a lot of input parameters have to be fine-tuned in order to obtain good results. To brain the right combination of input parameters for a specific problem is a hard task usually performed by data mining expert. So, to make decision tree based data mining end user friendly we explored various alternatives of decision tree induction, concentrating on purity measures. We introduced new hybrid purity measures and tested their adequacy on real world databases. Additionally we constructed a meta decision tree to determine the best combination of input parameters.