Combining Varied Learners for Binary Classification using Stacked Generalization
This work addresses classification challenges in medical data for researchers, but it is incremental as it applies an existing ensemble method to a new dataset.
The paper tackled binary classification on a high-dimensional Polycystic Ovary Syndrome dataset using Stacked Generalization, showing that the model becomes generalized with significantly improved metrics, including correcting an error in the Receiver Operating Characteristic Curve.
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.