Improving the Performance of an Associative Classifier in the Context of Class-Imbalanced Classification

Carlos Alberto Rolón-González,Antonio Alarcón-Paredes,Cornelio Yáñez-Márquez,Itzamá López-Yáñez,Rodrigo Castañón-Méndez

doi:10.3390/electronics10091095

Carlos Alberto Rolón-González, Antonio Alarcón-Paredes + Show 3 more

Open Access

https://doi.org/10.3390/electronics10091095

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Journal: Electronics	Publication Date: May 6, 2021
License type: CC BY 4.0

Affiliation: Instituto Politécnico Nacional

Abstract

Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus failing to correctly classify a minority class. Associative memories are models used for pattern recall; however, they can also be employed for pattern classification. In this paper, a novel method for improving the classification performance of a hybrid associative classifier with translation (better known by its acronym in Spanish, CHAT) is presented. The extreme center points (ECP) method modifies the CHAT algorithm by exploring alternative vectors in a hyperspace for translating the training data, which is an inherent step of the original algorithm. We demonstrate the importance of our proposal by applying it to imbalanced datasets and comparing the performance to well-known classifiers by means of the balanced accuracy. The proposed method not only enhances the performance of the original CHAT algorithm, but it also outperforms state-of-the-art classifiers in four of the twelve analyzed datasets, making it a suitable algorithm for classification in imbalanced class scenarios.

Highlights

Classification and recall are two important tasks that are performed in the context of the supervised paradigm of pattern recognition
Approaches and methods to classify patterns, such as Bayes, k-nearest neighbor (k-NN) classification, regression trees (CART), neural networks, support vector machines (SVM), and deep learning methods, among many others, have proliferated, and further improved classification algorithms can be commonly found in specialized literature [2]
In preliminary tests using CHAT-extreme center points (ECP), we observed that the election of different translation vectors is useful in imbalanced class scenarios

Summary

Introduction

Classification and recall are two important tasks that are performed in the context of the supervised paradigm of pattern recognition. The number of pattern recognition applications has grown significantly in recent years, and new areas of application continually appear In this context, every machine learning researcher who designs and creates a new pattern classifier algorithm hopes that the number of errors is as low as possible, and ideally the number of errors is zero, i.e., 100% performance; the proof of the no free lunch theorem precludes the existence of an ideal classifier. Every machine learning researcher who designs and creates a new pattern classifier algorithm hopes that the number of errors is as low as possible, and ideally the number of errors is zero, i.e., 100% performance; the proof of the no free lunch theorem precludes the existence of an ideal classifier This very important theorem governs the effectiveness of all pattern classification algorithms [3,4]

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