Improving the classification performance on imbalanced data sets via new hybrid parameterisation model

Abstract

The aim of this work is to analyse the performance of the new proposed hybrid parameterisation model in handling problematic data. Three types of problematic data will be highlighted in this paper: i) big data set, ii) uncertain and inconsistent data set and iii) imbalanced data set. The proposed hybrid model is an integration of three main phases which consist of the data decomposition, parameter reduction and parameter selection phases. Three main methods, which are soft set and rough set theories, were implemented to reduce and to select the optimised parameter set, while a neural network was used to classify the optimised data set. This proposed model can process a data set that might contain uncertain, inconsistent and imbalanced data. Therefore, one additional phase, data decomposition, was introduced and executed after the pre-processing task was completed in order to manage the big data issue. Imbalanced data sets were used to evaluate the capability of the proposed hybrid model in handling problematic data. The experimental results demonstrate that the proposed hybrid model has the potential to be implemented with any type of data set in a classification task, especially with complex data sets.