no code implementations • 23 Jan 2024 • Zardad Khan, Amjad Ali, Saeed Aldahmani
The performance of the proposed ROWSU method is evaluated on $6$ gene expression datasets.
no code implementations • 21 Mar 2023 • Amjad Ali, Muhammad Hamraz, Dost Muhammad Khan, Wajdan Deebani, Zardad Khan
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.
no code implementations • 21 Nov 2022 • Amjad Ali, Zardad Khan, Dost Muhammad Khan, Saeed Aldahmani
Each base model is constructed on a bootstrap sample with a random subset of features, and optimal models are selected based on out-of-bag performance after building a sufficient number of models.
no code implementations • 30 May 2022 • Amjad Ali, Muhammad Hamraz, Naz Gul, Dost Muhammad Khan, Zardad Khan, Saeed Aldahmani
kNN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting.
no code implementations • 11 Feb 2021 • Muhammad Naeem, Jian Yu, Muhammad Aamir, Sajjad Ahmad Khan, Olayinka Adeleye, Zardad Khan
Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic.
no code implementations • 30 Dec 2020 • Zardad Khan, Naz Gul, Nosheen Faiz, Asma Gul, Werner Adler, Berthold Lausen
The predictive performance of tree based machine learning methods, in general, improves with a decreasing rate as the size of training data increases.
no code implementations • 18 May 2020 • Naz Gul, Nosheen Faiz, Dan Brawn, Rafal Kulakowski, Zardad Khan, Berthold Lausen
The top ranked survival trees are then assessed for their collective performance as an ensemble.