Recent advances in the domain of software defect prediction (SDP) include the integration of
multiple classification techniques to create an ensemble or hybrid approach. This technique …

This paper presents a comprehensive review of evolutionary algorithms that learn an
ensemble of predictive models for supervised machine learning (classification and …

Social networks exhibit interactions that lead to event changes in their communities. It is
imperative to track community events to understand an extensive social network. Recently …

Highlights•A bi-objective algorithm is proposed for solving the multi-mode resource-
constrained multi-project scheduling problem.•The novel proposed algorithm demonstrates …

In the low-data regime, it is difficult to train good supervised models from scratch. Instead
practitioners turn to pre-trained models, leveraging transfer learning. Ensembling is an …

In this paper, we propose an evolutionary learning approach for flexible identification of
custom composite models for classification problems. To solve this problem in an efficient …

A large number of classification algorithms have been proposed in the machine learning
literature. These algorithms have different pros and cons, and no algorithm is the best for all …

Project scheduling is one of the core elements of project management, which is essential for
engineering-to-order manufacturing companies to improve productivity, reduce costs and …

Nowadays, a big pool of different machine learning components (ie, algorithms and tools)
exists that are capable of predicting various decisions in different problem domains …

Ensembles of classifiers are a very popular type of method for performing classification, due
to their usually high predictive accuracy. However, ensembles have two drawbacks. First …