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

Despite having notable advantages over established machine learning methods for time
series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet …

In a previous work, Villani et al. introduced a method to identify candidate emergent
dynamical structures in complex systems. Such a method detects subsets (clusters) of the …

Ensemble classifiers have become a widely researched area in machine learning because
they are able to generalise well to unseen data, making them suitable for real world …

Cardiac rhythm disorders may cause severe heart diseases, stroke, and even sudden
cardiac death. Some arrhythmias are so serious that can cause injury to other organs, for …

Ensemble classifiers are known for performing good generalization from simpler and less
accurate classifiers. Ensembles have the ability to use the variety in classification patterns of …

Ensemble learning is the machine learning paradigm that aims at integrating several base
learners into a single system under the assumption that the collective consensus …

Classification is the machine learning task of categorizing instances into classes. There are
several algorithms in the literature that perform classification, with varying degrees of …

Ensembles are groups of classifiers which cooperate in order to reach a decision.
Conventionally, the members of an ensemble are trained sequentially, and typically …

Kao nastavak prethodnih istraživanja autora, ova doktorska disertacija predstavlja sljedeći
korak istraživanja problema klasifikacije kreditnog rizika. Utemeljena na opservaciji …