The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
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
Data mining: practical machine learning tools and techniques with Java implementations
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Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
An introduction to boosting and leveraging
Advanced lectures on machine learning
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Boosted decision trees for diagnosis type of hypertension
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Probability Error in Global Optimal Hierarchical Classifier with Intuitionistic Fuzzy Observations
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Decomposition of classification task with selection of classifiers on the medical diagnosis example
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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The inductive learning approach could be immensely useful as the method generating effective classifiers. This paper presents idea of constructing two-stage classifier for diagnosis of the type of hypertension (essential hypertension and five type of secondary one: fibroplastic renal artery stenosis, atheromatous renal artery stenosis, Conn's syndrome, renal cystic disease and pheochromocystoma). The first step decides if patient suffers from essential hypertension or secondary one. This decision is made on the base on the decision of classifier obtained by boosted version of additive tree algorithm. The second step of classification decides which type of secondary hypertension patient is suffering from. The second step of classifier makes its own decision using human expert rules. The decisions of these classifiers are made only on base on blood pressure, general information and basis biochemical data.