A Review on Handling Imbalanced Data
Authors:
- 1 March 2018
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-11
Abstract
Computational synthesize of the metabolic pathway is take low cost while comparing with the direct trial and error laboratory process. In real world data, more or less all datasets having a skewed distribution of classes. The skewed and the number of instances for certain classes much higher than other classes, this problem is known as the class imbalance problem. Practically this class imbalance problem reduces the classification accuracy because it predicts the minority class instances inaccurately. Class imbalance is an issue encountered by data mining practitioners in a wide variety of fields. The classification of imbalanced data is a new problem that rises in the machine learning framework and it is the major problem raised for the researches and the use of sampling techniques to improve classification performance has received significant attention in related works. In this article the necessity of balancing an imbalanced data is elaborated and the methods proposed by the various authors for to balance the imbalanced data and the evaluation metrics to assess the accuracy and predictive rate of the classification algorithms also have been discussed.Keywords
This publication has 30 references indexed in Scilit:
- An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristicsInformation Sciences, 2013
- Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniquesPattern Recognition Letters, 2013
- SMOTE for high-dimensional class-imbalanced dataBMC Bioinformatics, 2013
- On the effectiveness of preprocessing methods when dealing with different levels of class imbalanceKnowledge-Based Systems, 2012
- The Research of Imbalanced Data Set of Sample Sampling Method Based on K-Means Cluster and Genetic AlgorithmEnergy Procedia, 2012
- A combined SMOTE and PSO based RBF classifier for two-class imbalanced problemsNeurocomputing, 2011
- Knowledge discovery from imbalanced and noisy dataData & Knowledge Engineering, 2009
- Cluster-based under-sampling approaches for imbalanced data distributionsExpert Systems with Applications, 2009
- Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-setsInternational Journal of Approximate Reasoning, 2009
- Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performanceNeural Networks, 2008