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Supervised link prediction in weighted networks

The aim is to investigate the relevance of using weights to improve supervised link prediction, and the preliminary results on supervised prediction on a co-authorship network revealed satisfactory results when weights were considered.
IEEE (opens in a new tab)

Ranking and selecting clustering algorithms using a meta-learning approach

A novel framework that applies a meta-learning approach to clustering algorithms that provides a ranking for the candidate algorithms that could be used with that dataset in the context of cancer gene expression micro-array datasets is presented.
IEEE (opens in a new tab)

Time Series Based Link Prediction

The preliminary results using two link prediction methods (unsupervised and supervised) on co-authorship networks revealed satisfactory results when temporal information was considered, and a forecasting model was deployed on these time series and used as the final scores of the pairs.
IEEE (opens in a new tab)

A multiple kernel learning algorithm for drug-_target interaction prediction

KronRLS-MKL is proposed, which models the drug-_target interaction problem as a link prediction task on bipartite networks and has higher or comparable predictive performance than 18 competing methods in all prediction tasks.
Springer (opens in a new tab)

Combining meta-learning and search techniques to select parameters for support vector machines

Publisher (opens in a new tab)

Proximity measures for link prediction based on temporal events

Publisher (opens in a new tab)

Meta-learning approaches to selecting time series models

Publisher (opens in a new tab)

Measuring Instance Hardness Using Data Complexity Measures

Experiments on synthetic and benchmark datasets demonstrate the proposed measures can provide a complementary instance hardness perspective when compared to those from related literature.
Publisher (opens in a new tab)

A Multi-objective Particle Swarm Optimization for Test Case Selection Based on Functional Requirements Coverage and Execution Effort

A mechanism for functional TC selection which considers two objectives simultaneously: maximize requirements' coverage while minimizing cost in terms of TC execution effort and two multi-objective versions of PSO are implemented.
IEEE (opens in a new tab)

Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors

In this work, we proposed the use of Support Vector Machines (SVM) to predict the performance of machine learning algorithms based on features of the learning problems. This work is related to the…
Publisher (opens in a new tab)
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