# Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method

arXiv: https://arxiv.org/abs/2202.07779 · HTML: https://mlsanity.com/paper/2202.07779/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [A Tutorial on Kernel Density Estimation and Recent Advances](https://mlsanity.com/paper/1704.03924/) (2017)
- [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)

## Built on by (most cited first, 8 of 8)

- [Combining Varied Learners for Binary Classification using Stacked Generalization](https://mlsanity.com/paper/2202.08910/) (2022)
- [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)
- [Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis](https://mlsanity.com/paper/2211.15196/) (2022)
- [Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification](https://mlsanity.com/paper/2206.02206/) (2022)
- [Metric Effects based on Fluctuations in values of k in Nearest Neighbor Regressor](https://mlsanity.com/paper/2208.11540/) (2022)
- [Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification](https://mlsanity.com/paper/2303.14401/) (2023)
- [A Multi-Component Reward Function with Policy Gradient for Automated Feature Selection with Dynamic Regularization and Bias Mitigation](https://mlsanity.com/paper/2510.09705/) (2025)

## Abstract (arXiv)

High dimensional data for classification does create many difficulties for machine learning algorithms. The generalization can be done using ensemble learning methods such as bagging based supervised non-parametric random forest algorithm. In this paper we solve the problem of binary classification for high dimensional data using random forest for polycystic ovary syndrome dataset. We have performed the implementation and provided a detailed visualization of the data for general inference. The training accuracy that we have achieved is 95.6% and validation accuracy over 91.74% respectively.