NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets

by   Soroush Saryazdi, et al.

Learning from many real-world datasets is limited by a problem called the class imbalance problem. A dataset is imbalanced when one class (the majority class) has significantly more samples than the other class (the minority class). Such datasets cause typical machine learning algorithms to perform poorly on the classification task. To overcome this issue, this paper proposes a new approach Neighbors Progressive Competition (NPC) for classification of imbalanced datasets. Whilst the proposed algorithm is inspired by weighted k-Nearest Neighbor (k-NN) algorithms, it has major differences from them. Unlike k- NN, NPC does not limit its decision criteria to a preset number of nearest neighbors. In contrast, NPC considers progressively more neighbors of the query sample in its decision making until the sum of grades for one class is much higher than the other classes. Furthermore, NPC uses a novel method for grading the training samples to compensate for the imbalance issue. The grades are calculated using both local and global information. In brief, the contribution of this paper is an entirely new classifier for handling the imbalance issue effectively without any manually-set parameters or any need for expert knowledge. Experimental results compare the proposed approach with five representative algorithms applied to fifteen imbalanced datasets and illustrate this algorithms effectiveness.


page 1

page 2

page 3

page 4


Graph Embedded Intuitionistic Fuzzy RVFL for Class Imbalance Learning

The domain of machine learning is confronted with a crucial research are...

Discriminative Sparse Neighbor Approximation for Imbalanced Learning

Data imbalance is common in many vision tasks where one or more classes ...

An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data

In this paper, we address the challenging problem of learning from imbal...

Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency

The class imbalance problem is important and challenging. Ensemble appro...

Neurochaos Feature Transformation and Classification for Imbalanced Learning

Learning from limited and imbalanced data is a challenging problem in th...

Signal classification using weighted orthogonal regression method

In this paper, a new classifier based on the intrinsic properties of the...

Instance-based entropy fuzzy support vector machine for imbalanced data

Imbalanced classification has been a major challenge for machine learnin...

Please sign up or login with your details

Forgot password? Click here to reset