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A Survey of Evolutionary Algorithms for Clustering

An up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering.
IEEE (opens in a new tab)

Data stream clustering: A survey

A survey of data stream clustering algorithms is presented, providing a thorough discussion of the main design components of state-of-the-art algorithms and an overview of the usually employed experimental methodologies.
ACM (opens in a new tab)

A review on the combination of binary classifiers in multiclass problems

This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems, and focuses on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.
Springer (opens in a new tab)

A Survey of Evolutionary Algorithms for Decision-Tree Induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction, which provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach.
IEEE (opens in a new tab)

Reduction strategies for hierarchical multi-label classification in protein function prediction

This work presents a new hierarchical multi-label classification method based on multiple neural networks for the task of protein function prediction that was able to learn the relationships between the protein functions during training, and this information was useful for classification.
Springer (opens in a new tab)

A Tutorial on Multi-label Classification Techniques

This tutorial presents the most frequently used techniques to deal with multi-label classification problems in a pedagogical manner, with examples illustrating the main techniques and proposing a taxonomy of multi- label techniques that highlights the similarities and differences between these techniques.
Publisher (opens in a new tab)

Node-Depth Encoding for Evolutionary Algorithms Applied to Network Design

This article proposes an encoding for generation of spanning forests by evolutionary algorithms and is evaluated for degree-constrained minimum spanning tree problem.
Publisher (opens in a new tab)

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

Publisher (opens in a new tab)

A review of performance evaluation measures for hierarchical classifiers

This paper reviews the main evaluation metrics proposed in the literature to evaluate hierarchical classification models and proposes several performance measures for binary and flat classification problems.

Spectral methods for graph clustering - A survey

Publisher (opens in a new tab)
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