In this paper, a comparative analysis between four of the most common under-sampling techniques is conducted over datasets with various imbalance rates (IR) range from low to medium to high IR. Decision Tree classifier and twelve imbalanced data sets with various IR a...
An empirical comparison of repetitive undersampling techniques A common problem for data mining and machine learning practitioners is class imbalance. When examples of one class greatly outnumber examples of the other ... JV Hulse,TM Khoshgoftaar,A Napolitano - Information Reuse & Integration 被引...
https://machinelearningmastery.com/failure-of-accuracy-for-imbalanced-class-distributions/ Try a range of models and imbalanced learning techniques and discover what results in the best performance for your dataset. Controlled experiments is the only path forward. Reply San...
Sampling and estimation techniques for the implementation of new classification systems: The change-over from NACE Rev. 1.1 to NACE Rev. 2 in business surveys (2010), Sampling and estimation techniques for the implementation of new classification systems: the change-over from NACE Rev. 1.1 to NAC...
Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China Sci. Total Environ., 625 (2018), pp. 575-588 View PDFView articleView in ScopusGoogle Scholar Hong et al., 2018b H. Hong, P. Tsangaratos, I. Ilia, J...
Theclassificationtechniquesusuallyassumethatthetraining samplesareuniformlydistributedbetweendifferentclasses.A classifierperformswellwhentheclassificationtechniqueisap- pliedtoadatasetevenlydistributedamongdifferentclasses.How- ever,manydatasetsinrealapplicationsinvolveimbalancedclass ...
In third place, cost-sensitive approaches [10], [17] combine the mentioned techniques in such a way that they incorporate different misclassification costs for the instances in the learning algorithm. Finally, ensemble solutions [12], [49], [47] try to combine one of the previous approaches ...
Improving Cardiovascular Disease Prediction by Integrating Imputation, Imbalance Resampling, and Feature Selection Techniques into Machine Learning Model Integration of multiple imputation by chained equation (MICE) and synthetic minority oversampling with Tomek link down-sampling (SMOTETomek) into the model ...
Various techniques in machine learning have been used for building software defect prediction (SDP) models to identify the defective software modules. Howe
Due to the fact that the experimental techniques for identifying APPs are expensive and time-consuming, there is an urgent need to develop a computational approach to predict APPs on a large scale. In this study, we provided a computational method, termed PredAPP (Prediction of Anti-Parasitic ...