MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and Improvements
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
| Hi-index | 0.00 |
Gene conversion, a non-reciprocal transfer of genetic information from one sequence to another, is a biological process whose importance in affecting both short-term and long-term evolution cannot be overemphasized. Knowing where gene conversion has occurred gives us important insights into gene duplication and evolution in general. In this paper we present an ensemble-based learning method for predicting gene conversions using two different models of reticulate evolution. Since detecting gene conversion is a rare-class problem, we implement cost-sensitive learning in the form of a generated cost matrix that is used to modify various underlying classifiers. Results show that our method combines the predictive power of different models and is able to predict gene conversion more accurately than any of the two studied models. Our work provides a useful framwork for future improvement of gene conversion predictions through multiple models of gene conversion.