A hybrid approach for efficient outlier detection using supervised and unsupervised techniques

Abstract

Abstract Due to data imbalance and dimensionality, it is difficult to achieve optimal performance when detecting outliers in high-dimensional data. Numerous algorithms were developed in try to solve this issue. However, they have their advantages in identifying outliers from such data and are created using either supervised learning technique or unsupervised learning. While unsupervised learning techniques offer mechanisms for discovering and utilising complicated patterns, supervised learning techniques make use of training data. This paper's key premise is that you may "combine two methodologies to create a hybrid and reap the benefits of both worlds." We put forth a cutting-edge machine learning (ML) framework to evaluate this claim, combining supervised and unsupervised techniques for effective outlier detection. Additionally, we suggested an approach called the Multi-Model Approach for Outlier Detection (MMA-OD). The technique improves performance by utilising the advantages of both supervised and unsupervised learning models. Its strength is getting a better feature space. With several benchmark datasets, the suggested approach is assessed. According to the empirical findings, MMA-OD performs better than many other techniques.