Survey on deep learning with class imbalance
نویسندگان
چکیده
منابع مشابه
Class Imbalance Learning
This report presents the work completed since the thesis proposal and the revised plan for the future PhD study. Two main issues have been discussed so far: diversity analysis of ensemble models in class imbalance learning, exploration of negative correlation learning on imbalanced data. Experimental design and main conclusions are simply described. More details are included in the two papers i...
متن کاملClass Imbalance and Active Learning
The rich history of predictive modeling has culminated in a diverse set of techniques capable of making accurate predictions on many real-world problems. Many of these techniques demand training, whereby a set of instances with ground-truth labels (values of a dependent variable) are observed by a model-building process that attempts to capture, at least in part, the relationship between the fe...
متن کاملDynamic class imbalance learning for incremental LPSVM
Linear Proximal Support Vector Machines (LPSVMs), like decision trees, classic SVM, etc. are originally not equipped to handle drifting data streams that exhibit high and varying degrees of class imbalance. For online classification of data streams with imbalanced class distribution, we propose a dynamic class imbalance learning (DCIL) approach to incremental LPSVM (IncLPSVM) modeling. In doing...
متن کاملTowards Class-Imbalance Aware Multi-Label Learning
In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i.e. the widely-exis...
متن کاملEnsemble diversity for class imbalance learning
This thesis studies the diversity issue of classification ensembles for class imbalance learning problems. Class imbalance learning refers to learning from imbalanced data sets, in which some classes of examples (minority) are highly under-represented comparing to other classes (majority). The very skewed class distribution degrades the learning ability of many traditional machine learning meth...
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ژورنال
عنوان ژورنال: Journal of Big Data
سال: 2019
ISSN: 2196-1115
DOI: 10.1186/s40537-019-0192-5