Volume 12, Number 2
Enactment Ranking of Supervised Algorithms Dependence of Data Splitting Algorithms:
A Case Study of Real Datasets
Hina Tabassum and Muhammad Mutahir Iqbal, Bahauddin Zakariya University, Pakistan
We conducted comparative analysis of different supervised dimension reduction techniques by integrating a set of different data splitting algorithms and demonstrate the relative efficacy of learning algorithms dependence of sample complexity. The issue of sample complexity discussed in the dependence of data splitting algorithms. In line with the expectations, every supervised learning classifier demonstrated different capability for different data splitting algorithms and no way to calculate overall ranking of techniques was directly available. We specifically focused the classifier ranking dependence of data splitting algorithms and devised a model built on weighted average rank Weighted Mean Rank Risk Adjusted Model (WMRRAM) for consent ranking of learning classifier algorithms.
Supervised Learning Algorithms, Data Splitting Algorithms, Ranking, Weighted Mean rank risk-adjusted Model