Class Imbalance Learning
نویسندگان
چکیده
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 in the ‘Appendices’ section. In addition, this report introduces some new ideas and related literature review – observational learning algorithm (OLA). Finally, the research plan is modified in a small range because further experiments of current work are needed, and followed by PhD thesis table of content.
منابع مشابه
MMDT: Multi-Objective Memetic Rule Learning from Decision Tree
In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...
متن کاملOnline Class Imbalance Learning and its Applications in Fault Detection
Although class imbalance learning and online learning have been extensively studied in the literature separately, online class imbalance learning that considers the challenges of both ̄elds has not drawn much attention. It deals with data streams having very skewed class distributions, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. T...
متن کاملA Review of Class Imbalance Problem
Class imbalance is one of the challenges of machine learning and data mining fields. Imbalance data sets degrades the performance of data mining and machine learning techniques as the overall accuracy and decision making be biased to the majority class, which lead to misclassifying the minority class samples or furthermore treated them as noise. This paper proposes a general survey for class im...
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...
متن کاملA comparative study on rough set based class imbalance learning
This paper performs systematic comparative studies on rough set based class imbalance learning. We compare the strategies of weighting, re-sampling and filtering used in the rough set based methods for class imbalance learning. Weighting is better than re-sampling, and re-sampling is better than filtering. The weighted rough set based method achieves the best performance in class imbalance lear...
متن کاملUsing Class Imbalance Learning for Cross-Company Defect Prediction
Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, the performance of such CCDP models is susceptible to the high imbalanced nature between the defect-prone and non-defect classes of CC data. Class imbalance learning is applied to alleviat...
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