Parameter-free classification in multi-class imbalanced data sets
نویسندگان
چکیده
منابع مشابه
Parameter-free classification in multi-class imbalanced data sets
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau...
متن کاملMulti-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning
In a classification task, the imbalance class problem is present when the data-set has a very different distribution of examples among their classes. The main handicap of this type of problem is that standard learning algorithms consider a balanced training set and this supposes a bias towards the majority classes. In order to provide a correct identification of the different classes of the pro...
متن کاملA Review of Multi-Class Classification for Imbalanced Data
Prediction and correct voting is critical task in imbalance data multi-class classification. Accuracy and performance of multi-class depends on voting and prediction of new class data. Assigning of new class of imbalance data generate confusion and decrease the accuracy and performance of classifier. Various authors and research modified the multiclass classification approach such as one agains...
متن کاملEvaluating Difficulty of Multi-class Imbalanced Data
Multi-class imbalanced classification is more difficult than its binary counterpart. Besides typical data difficulty factors, one should also consider the complexity of relations among classes. This paper introduces a new method for examining the characteristics of multi-class data. It is based on analyzing the neighbourhood of the minority class examples and on additional information about sim...
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 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: Data & Knowledge Engineering
سال: 2013
ISSN: 0169-023X
DOI: 10.1016/j.datak.2013.06.001