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
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Modifying boosted trees to improve performance on task 1 of the 2006 KDD challenge cup
ACM SIGKDD Explorations Newsletter
Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
This paper presents our solution for KDD Cup 2008 competition that aims at optimizing the area under ROC for breast cancer detection. We exploited weighted-based classification mechanism to improve the accuracy of patient classification (each patient is represented by a collection of data points). Final predictions for challenge 1 are generated by combining outputs from weighted SVM and AdaBoost; whereas we integrate SVM, AdaBoost, and GA to produce the results for challenge 2. We have also tried location-based classification and model adaptation to add the testing data into training. Our results outperform other participants given the same set of features, and was selected as the joint winner in KDD Cup 2008.