2 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics
Zardad Khan, Amjad Ali, Naz Gul +2
Objective: Small-sample molecular classification requires feature selectors that identify predictive, stable, and nonredundant subsets for binary and multiclass outcomes. We propos…
Centroid Decision Forest
Amjad Ali, Saeed Aldahmani, Hailiang Du +1
This paper introduces the centroid decision forest (CDF), a novel ensemble learning framework that redefines the splitting strategy and tree building in the ordinary decision trees…
Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data
Zardad Khan, Amjad Ali, Saeed Aldahmani
In this paper, a robust weighted score for unbalanced data (ROWSU) is proposed for selecting the most discriminative feature for high dimensional gene expression binary classificat…
A Random Projection k Nearest Neighbours Ensemble for Classification via Extended Neighbourhood Rule
Amjad Ali, Muhammad Hamraz, Dost Muhammad Khan +2
Ensembles based on k nearest neighbours (kNN) combine a large number of base learners, each constructed on a sample taken from a given training data. Typical kNN based ensembles de…
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak
Muhammad Naeem, Jian Yu, Muhammad Aamir +3
Background. Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness…
Optimal trees selection for classification via out-of-bag assessment and sub-bagging
Zardad Khan, Naz Gul, Nosheen Faiz +3
The effect of training data size on machine learning methods has been well investigated over the past two decades. The predictive performance of tree based machine learning methods…