Instance-Based Learning Algorithms
Machine Learning
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ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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ACM SIGART Bulletin
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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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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ICML '05 Proceedings of the 22nd international conference on Machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Lazy Bagging for Classifying Imbalanced Data
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Nearest neighbor editing aided by unlabeled data
Information Sciences: an International Journal
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In this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on the characteristics of test instances. Upon receiving a test instance x"k, LB trims bootstrap bags by taking into consideration x"k's nearest neighbors in the training data. Our hypothesis is that an unlabeled instance's nearest neighbors provide valuable information to enhance local learning and generate a classifier with refined decision boundaries emphasizing the test instance's surrounding region. In particular, by taking full advantage of x"k's nearest neighbors, classifiers are able to reduce classification bias and variance when classifying x"k. As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach. To investigate LB's performance, we first use carefully designed synthetic data sets to gain insight into why LB works and under which conditions it can outperform TB. We then test LB against four rival algorithms on a large suite of 35 real-world benchmark data sets using a variety of statistical tests. Empirical results confirm that LB can statistically significantly outperform alternative methods in terms of reducing classification error.