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Found 9 papers, 0 papers with code
Multi-objective Semi-supervised Clustering for Finding Predictive Clusters
This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable.
A new interval-based aggregation approach based on bagging and Interval Agreement Approach (IAA) in ensemble learning
In this paper, in addition to implementing a new aggregation approach in ensemble learning, we designed some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and this leads to more accurate classification.
On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models
This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making.
Similarity measure for aggregated fuzzy numbers from interval-valued data
The similarity measure proposed within this study contains several features and attributes, of which are novel to aggregated fuzzy numbers.
Methods of ranking for aggregated fuzzy numbers from interval-valued data
The two proposed ranking methods within this study contain the combination and application of previously proposed similarity measures, along with attributes novel to that of aggregated fuzzy numbers from interval-valued data.
Transfer learning to enhance amenorrhea status prediction in cancer and fertility data with missing values
Also, missing values are unavoidable in health and medical datasets and tackling the problem arising from the inadequate instances and missingness is not straightforward (Snell, et al. 2017, Sterne, et al. 2009).
Imputation techniques on missing values in breast cancer treatment and fertility data
Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years.
Multi-objective semi-supervised clustering to identify health service patterns for injured patients
The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury.
Uncertainty measures for probabilistic hesitant fuzzy sets in multiple criteria decision making
This contribution reviews critically the existing entropy measures for probabilistic hesitant fuzzy sets (PHFSs), and demonstrates that these entropy measures fail to effectively distinguish a variety of different PHFSs in some cases.
