C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Use of built-in features in the interpretation of high-dimensional cancer diagnosis data
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
A comparative study of classification methods for microarray data analysis
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
A maximally diversified multiple decision tree algorithm for microarray data classification
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Boosting recombined weak classifiers
Pattern Recognition Letters
Evolutionary approach to combined multiple models tuning
International Journal of Knowledge-based and Intelligent Engineering Systems - Extended papers selected from KES-2006
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Evolutionary tuning of combined multiple models
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Post mining of diversified multiple decision trees for actionable knowledge discovery
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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We introduce a new method, called CS4, to constructcommittees of decision trees for classification. The methodconsiders different top-ranked features as the root nodes ofmember trees. This idea is particularly suitable for dealingwith high-dimensional bio-medical data as top-ranked featuresin this type of data usually possess similar merits forclassification. To make a decision, the committee combinesthe power of individual trees in a weighted manner. UnlikeBagging or Boosting which uses bootstrapped trainingdata, our method builds all the member trees of a committeeusing exactly the same set of training data. We have testedthese ideas on UCI data sets as well as recent bio-medicaldata sets of gene expression or proteomic profiles that areusually described by more than 10,000 features. All the experimentalresults show that our method is efficient and thatthe classification performance are superior to C4.5 familyalgorithms.