4 citations · 4 across the 8 of their papers we have counts for
8 papers
A new interval-based aggregation approach based on bagging and Interval Agreement Approach (IAA) in ensemble learning
Mansoureh Maadia, Uwe Aickelin, Hadi Akbarzadeh Khorshidi
The main aim in ensemble learning is using multiple individual classifiers outputs rather than one classifier output to aggregate them for more accurate classification. Generating…
On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models
Yuxuan, Yang, Hadi Akbarzadeh Khorshidi +3
Surgical risk increases significantly when patients present with comorbid conditions. This has resulted in the creation of numerous risk stratification tools with the objective of…
Machine learning with incomplete datasets using multi-objective optimization models
Hadi A. Khorshidi, Michael Kirley, Uwe Aickelin
Machine learning techniques have been developed to learn from complete data. When missing values exist in a dataset, the incomplete data should be preprocessed separately by removi…
Similarity measure for aggregated fuzzy numbers from interval-valued data
Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin
This paper presents a method to compute the degree of similarity between two aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The similarity mea…
Methods of ranking for aggregated fuzzy numbers from interval-valued data
Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin
This paper primarily presents two methods of ranking aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The two proposed ranking methods within th…
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values
Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin +2
Collecting sufficient labelled training data for health and medical problems is difficult (Antropova, et al., 2018). Also, missing values are unavoidable in health and medical data…
Multicriteria Group Decision-Making Under Uncertainty Using Interval Data and Cloud Models
Hadi A. Khorshidi, Uwe Aickelin
In this study, we propose a multicriteria group decision making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates th…
A Synthetic Over-sampling method with Minority and Majority classes for imbalance problems
Hadi A. Khorshidi, Uwe Aickelin
Class imbalance is a substantial challenge in classifying many real-world cases. Synthetic over-sampling methods have been effective to improve the performance of classifiers for i…