Combining Varied Learners for Binary Classification using Stacked Generalization

TL;DR

This paper demonstrates that stacking ensemble methods improve binary classification performance on high-dimensional datasets, specifically for Polycystic Ovary Syndrome, and addresses issues with ROC curve interpretation.

Contribution

It applies stacked generalization to high-dimensional medical data and uncovers a subtle error in ROC curve analysis.

Findings

Improved classification metrics with stacking ensemble.

Validation of model generalization on high-dimensional data.

Identification of a flaw in ROC curve interpretation.

Abstract

The Machine Learning has various learning algorithms that are better in some or the other aspect when compared with each other but a common error that all algorithms will suffer from is training data with very high dimensional feature set. This usually ends up algorithms into generalization error that deplete the performance. This can be solved using an Ensemble Learning method known as Stacking commonly termed as Stacked Generalization. In this paper we perform binary classification using Stacked Generalization on high dimensional Polycystic Ovary Syndrome dataset and prove the point that model becomes generalized and metrics improve significantly. The various metrics are given in this paper that also point out a subtle transgression found with Receiver Operating Characteristic Curve that was proved to be incorrect.