Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Pattern recognition using type-II fuzzy sets
Information Sciences—Informatics and Computer Science: An International Journal
Fuzzy Sets and Systems
Boosting with averaged weight vectors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
On the Dual Formulation of Boosting Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosted decision trees for diagnosis type of hypertension
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Imprecise information in Bayes classifier
Pattern Analysis & Applications
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This paper presents a new extension of AdaBoost algorithm based on interval-valued fuzzy sets. This extension is for the weights used in samples of the training sets. The original weights are the real number from the interval [0, 1]. In our approach the weights are represented by the interval-valued fuzzy set, that is any weight has a lower and upper membership function. The same value of lower and upper membership function has a weight of the appropriate weak classifier. In our study we use the boosting by the reweighting method where each weak classifier is based on the recursive partitioning method. The described algorithm was tested on two generation data sets and two sets from UCI repository. The obtained results are compared with the original AdaBoost algorithm.