The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
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
Data mining: practical machine learning tools and techniques with Java implementations
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Computer Vision and Fuzzy-Neural Systems
Computer Vision and Fuzzy-Neural Systems
Machine Learning
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts
Journal of Medical Systems
Artificial Intelligence in Medicine
An efficient modified boosting method for solving classification problems
Journal of Computational and Applied Mathematics
Computer-Based Identification of Breast Cancer Using Digitized Mammograms
Journal of Medical Systems
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural
Expert Systems with Applications: An International Journal
A data pre-processing method to increase efficiency and accuracy in data mining
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Technology in Biomedicine
A multilayered ensemble architecture for the classification of masses in digital mammograms
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Methods that can accurately predict breast cancer are greatly needed and good prediction techniques can help to predict breast cancer more accurately. In this study, we used two feature selection methods, forward selection (FS) and backward selection (BS), to remove irrelevant features for improving the results of breast cancer prediction. The results show that feature reduction is useful for improving the predictive accuracy and density is irrelevant feature in the dataset where the data had been identified on full field digital mammograms collected at the Institute of Radiology of the University of Erlangen-Nuremberg between 2003 and 2006. In addition, decision tree (DT), support vector machine--sequential minimal optimization (SVM-SMO) and their ensembles were applied to solve the breast cancer diagnostic problem in an attempt to predict results with better performance. The results demonstrate that ensemble classifiers are more accurate than a single classifier.