Work

Improving Binary Classification Accuracy Using Stacked Ensemble of Diverse Models

Year: 2025

Type: article

Abstract: Every Machine Learning algorithm has some advantages and disadvantages of it's own, but all have the common error of high -dimensional feature set overfitting the training data. This causes depletion in performance by driving algorithm into generalization error. One of the Ensemble Learning methods called Stacking or Stacked Generalization can solve this problem. In this paper we carry out binary classification using Stacked Generalization on high dimensional Polycystic Ovary Syndrome dataset and demonstrate that model generalizes and metrics such as accuracy improve substantially. There are several other metrics which in my opinion provide a glaring pg57 with Receiver Operating Characteristic Curve which provides evidence of incorrectness.

Source: International Journal of Science and Research (IJSR)

DOI: 10.21275/sr25423222250

Authors: Vidya Chitre, Sruthi Nair

Institution: Vidyalankar Institute of Technology

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4409843882