# Combining Varied Learners for Binary Classification using Stacked Generalization

arXiv: https://arxiv.org/abs/2202.08910 · HTML: https://mlsanity.com/paper/2202.08910/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Activation Functions: Comparison of trends in Practice and Research for Deep Learning](https://mlsanity.com/paper/1811.03378/) (2018)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Generalization Error in Deep Learning](https://mlsanity.com/paper/1808.01174/) (2018)
- [Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis](https://mlsanity.com/paper/2201.00418/) (2022)
- [Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication](https://mlsanity.com/paper/2201.03029/) (2022)
- [A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2009.13935/) (2020)
- [Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method](https://mlsanity.com/paper/2202.07779/) (2022)

## Built on by (most cited first, 4 of 4)

- [Detection of Tool based Edited Images from Error Level Analysis and Convolutional Neural Network](https://mlsanity.com/paper/2204.09075/) (2022)
- [Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification](https://mlsanity.com/paper/2205.12775/) (2022)
- [Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification](https://mlsanity.com/paper/2206.02206/) (2022)
- [Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification](https://mlsanity.com/paper/2303.14401/) (2023)

## Abstract (arXiv)

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.