Learning SVM with weighted maximum margin criterion for classification of imbalanced data
نویسندگان
چکیده
منابع مشابه
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
As a kernel-based method, whether the selected kernel matches the data determines the performance of support vector machine. Conventional support vector classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority class which is the less important class. In this paper, we propose a weighted maximum margin criterion to optimize the data-...
متن کاملLocal Relevance Weighted Maximum Margin Criterion for Text Classification
Text classification is a very important task in information retrieval and data mining. In vector space model (VSM), document is represented as a high dimensional vector, and a feature extraction phase is usually needed to reduce the dimensionality of the document. In this paper, we propose a feature extraction method, named Local Relevance Weighted Maximum Margin Criterion (LRWMMC). It aims to ...
متن کاملOn Mining Fuzzy Classification Rules for Imbalanced Data
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 ...
متن کاملOn Mining Fuzzy Classification Rules for Imbalanced Data
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 ...
متن کاملLaplacian Maximum Margin Criterion for Image Recognition
Previous works have demonstrated that Laplacian embedding can well preserve the local intrinsic structure. However, it ignores the diversity and may impair the local topology of data. In this paper, we build an objective function to learn the local intrinsic structure that characterizes both the local similarity and diversity of data, and then combine it with global structure to build a scatter...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematical and Computer Modelling
سال: 2011
ISSN: 0895-7177
DOI: 10.1016/j.mcm.2010.11.040