Borderline over-sampling for imbalanced data classification
نویسندگان
چکیده
منابع مشابه
Borderline over-sampling for imbalanced data classification
Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through oversampling at the borderline b...
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Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...
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ژورنال
عنوان ژورنال: International Journal of Knowledge Engineering and Soft Data Paradigms
سال: 2011
ISSN: 1755-3210,1755-3229
DOI: 10.1504/ijkesdp.2011.039875