Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification

Abstract

The main objective of this research is to improve the predictive accuracy of classification in ordinal multiclass imbalanced scenario. The methodology attempts to uplift the classifier performance through synthesizing sophisticated objects of immature classes.  A novel Adaptive Data Structure based oversampling algorithm is proposed to create synthetic objects and Extreme Learning Machine for Ordinal Regression (ELMOP) classifier is adopted to validate our work.   The proposed method generating new objects by analyzing the characteristics and intricacy of immature class objects. On the whole, the data set is divided into training and test data. Training data set is updated with new synthetic objects.  The experimental analysis is performed on testing data set to check the efficiency of the proposed methodology by comparing it with the existing work.    The performance evaluation is conducted in terms of the parameters called Mean Absolute Error, Maximum Mean Absolute Error, Geometric Mean, Kappa and Average Accuracy.  The measures prove that the proposed methodology can produce authentic synthetic objects than the existing techniques.  The Proposed technique can synthesize the new effective objects through evaluating the structure of immature class.  It boosts the global precision and class wise precision especially preserves rank order of the classes.