Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method
This work addresses classification challenges in high-dimensional medical data, but it is incremental as it applies an existing method to a new dataset.
The paper tackled binary classification for high-dimensional data using a random forest ensemble method on a polycystic ovary syndrome dataset, achieving a training accuracy of 95.6% and validation accuracy of 91.74%.
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.