A proposed method to validate anomalies detected with unsupervised models

This paper introduces a methodology aimed at validating anomalies identified through unsupervised techniques. Our approach is grounded in the assumption that machine learning models perform optimally when trained on data free of anomalies. Therefore, to assess the veracity of anomalies pinpointed by an unsupervised method, we undertake a two-fold process. Initially, anomalies are removed from the training dataset. Subsequently, we gauge the expected enhancement in the performance of classification models. To evaluate the effectiveness of our methodology, we employed three well-established unsupervised anomaly detection techniques: Local Outlier Factor (LOF), Isolation Forest (iForest), and Autoencoders. These methods were complemented by a voting system designed to identify anomalous data records. The reliability of these detected anomalies was rigorously tested using various classification models, including K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Classifier (SVC). This evaluation was conducted both before and after the removal of anomalies from the training dataset. Our methodology underwent rigorous testing across five distinct datasets: Breast Cancer, German Credit, Diabetes, Heart Failure Disease, and Titanic Survivor. The results provide evidence of its effectiveness, with notable improvements observed in the classification performance across four of the five datasets.