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The issue of incomplete data exists across the entire field of data mining. In this paper, a novel two-phase method is developed to deal with the challenge of incomplete data on classification problems. In phase I, the dataset is divided into disjoint subsets based on the attributes with missing values. In phase II, each subset is used to train appropriate classification algorithms respectively in...
Alzheimer's disease (AD) is one of the most common forms of dementia and has become a serious issue among the elderly in the aging society. Since AD is incurable and degenerative, early diagnosis is essential, which can give patients and their family more opportunities to arrange their lives. In the meantime, histopathologic studies have found that MCI (mild cognitive impairment) subjects usually...
Rare class problems exist extensively in real-world applications across a wide range of domains. The extreme scarcity of the target class challenges traditional machine learning algorithms focusing on the overall classification accuracy. As a result, purposefully designed techniques are required for effectively solving the rare class mining problem. This paper presents a systematic review of the major...
This paper investigates an interesting question of solving incremental learning problems using ensemble algorithms. The motivation is to help classifiers learn additional information from new batches of data incrementally while preserving previously acquired knowledge. Experimental results show that the proposed dynamic weighting scheme can achieve better performance compared to the fixed weighting...
This paper presents some preliminary experimental results on RegionBoost, which is a typical example of a class of boosting algorithms based on dynamic weighting schemes. It is shown that the performance of RegionBoost with the k-nearest neighbor (kNN) algorithm as the competency predictor of its basic classifiers can be significantly improved on a variety of standard UCI benchmark datasets by using...
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