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
Machine Learning
Pick your neighborhood: improving labels and neighborhood structure for label propagation
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
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Alzheimer's disease (AD) is the most common form of dementia and leads to irreversible neurogenerative damage of the brain. However, the current diagnostic tools have poor sensitivity, especially for the early stages of AD and do not allow for diagnosis until AD has lead to irreversible brain damage. Therefore, it is crucial that AD is detected as early as possible. Although it is very hard, laborious and time-consuming to gather many AD and non-AD labeled samples, gathering unlabeled samples is easier than labeled samples. Since standard learning algorithms learn a diagnosis model from labeled samples only, they require many labeled samples and do not work well when the number of training samples is small. Therefore, it is very desirable to develop a predictive learning method to achieve high performance using both labeled samples and unlabeled samples. To address these problems, we propose semi-supervised distance metric learning using Random Forests with label propagation (SRF-LP) which incorporates labeled data for obtaining good metrics and propagates labels based on them. Experimental results showed that SRF-LP outperformed standard supervised learning algorithms, i.e., RF, SVM, Adaboost and CART and reached 93.1% accuracy at a maximum. Especially, SRF-LP largely outperformed when the number of training samples is very small. Our results also suggested that SRF-LP exhibits a synergistic effect of semi-supervised distance metric learning and label propagation.