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Butt, Muheet
- A Framework for Class Imbalance Problem Using Hybrid Sampling
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PDF Views:6
Authors
Affiliations
1 University of Kashmir, IN
2 Dept. of Computer Science, University of Kashmir, IN
1 University of Kashmir, IN
2 Dept. of Computer Science, University of Kashmir, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 4 (2018), Pagination: 83-89Abstract
The skewness in underlying data distribution is natural in most of the datasets that are generated in real world applications and such datasets are commonly known as class imbalanced datasets. The examples of one class are very less in number than the examples in other class(es).The multi-class classification with imbalanced datasets has grabbed much attention by data mining and machine learning research communities in recent years. The main aim of this paper is to ameliorate the classification performance of minority class without reducing the classification performance of majority class(es).The problem is studied as one while as usually the researchers have studied it as two individual problems multi-class and imbalance problem.
This paper addresses the problem by devising a novel framework based on data solution (Random Hybrid Sampling) and well known binarization algorithm (OVO-Binarization).Eventually performance improvement of our frame work is shown using several performance measures such as Precision,Recall,F1-score and G-Mean on benchmark data-sets imported from UCI machine learning repository.
Keywords
Class Imbalance, Classification, Over-Sampling, Under-Sampling, OVO-Binarization, Performance Metrics.
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- A Comprehensive Review on Class Imbalance Problem
Abstract Views :332 |
PDF Views:4
Authors
Affiliations
1 University of Kashmir, IN
2 Dept. of Computer Science, University of Kashmir, IN
1 University of Kashmir, IN
2 Dept. of Computer Science, University of Kashmir, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 3 (2018), Pagination: 59-65Abstract
Classification of imbalanced data distribution using the standard learning algorithms which assume a relatively equal misclassification costs and relatively balanced underlying class distribution has encountered a serious drawbacks. This paper presents a comprehensive review of learning from Class imbalanced data. Our aim is to provide a review of the class imbalance problem, the state-of-art techniques and the performance measurement metrics used for evaluation under class imbalance scenario. Class imbalance problem in presence of multiple classes is also discussed.