Name
Abstract | ||
|---|---|---|
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. The significant difficulty and frequent occurrence of the class imbalance problem indicate the need for extra research efforts. The objective of this paper is to investigate meta-techniques applicable to most classifier learning algorithms, with the aim to advance the classification of imbalanced data. The AdaBoost algorithm is reported as a successful meta-technique for improving classification accuracy. The insight gained from a comprehensive analysis of the AdaBoost algorithm in terms of its advantages and shortcomings in tacking the class imbalance problem leads to the exploration of three cost-sensitive boosting algorithms, which are developed by introducing cost items into the learning framework of AdaBoost. Further analysis shows that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss. These boosting algorithms are also studied with respect to their weighting strategies towards different types of samples, and their effectiveness in identifying rare cases through experiments on several real world medical data sets, where the class imbalance problem prevails. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.patcog.2007.04.009 | Pattern Recognition |
Keywords | Field | DocType |
application domain,well-developed classification system,class imbalance problem,data mining,adaboost algorithm,accuracy improvement,cost item,imbalanced class distribution,classification accuracy,proposed algorithms tally,cost value,proposed cost-sensitive,cost-sensitive learning,classification,cost exponential loss,effective cost value,adaboost,medical data set,balanced class distribution,imbalanced data,comprehensive analysis | Data mining,Weighting,Data set,Artificial intelligence,Classifier (linguistics),BrownBoost,Tacking,AdaBoost,Pattern recognition,Boosting (machine learning),LPBoost,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
40 | 12 | Pattern Recognition |
Citations | PageRank | References |
403 | 9.80 | 74 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanmin Sun | 1 | 770 | 21.67 |
| Mohamed S. Kamel | 2 | 4523 | 282.55 |
| Andrew K. C. Wong | 3 | 4063 | 518.39 |
| Yang Wang | 4 | 948 | 155.42 |