Noise reduction for instance-based learning with a local maximal margin approach
نویسندگان
چکیده
منابع مشابه
A Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems
Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the pro...
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fuzzy rule-based classification systems (frbcs) are highly investigated by researchers due to their noise-stability and interpretability. unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. most of the pro...
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Multi-instance learning (MIL) considers input as bags of instances, in which labels are assigned to the bags. MIL is useful in many real-world applications. For example, in image categorization semantic meanings (labels) of an image mostly arise from its regions (instances) instead of the entire image (bag). Existing MIL methods typically build their models using the Bag-to-Bag (B2B) distance, ...
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Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning applications. However, these learning algorithms are highly sensitive to noisy training instances. This paper describes a simple extension of instancebased learning algorithms for detecting and removing noisy instances from conce...
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In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...
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ژورنال
عنوان ژورنال: Journal of Intelligent Information Systems
سال: 2009
ISSN: 0925-9902,1573-7675
DOI: 10.1007/s10844-009-0101-z