A survey on evolutionary machine learning

Al-Sahaf, Harith; Bi, Ying; Chen, Qi; Lensen, Andrew; Mei, Yi; Sun, Yanan; Tran, Binh; Xue, Bing; Zhang, Mengjie

doi:10.1080/03036758.2019.1609052

Cited by 174 publications

(34 citation statements)

References 169 publications

(173 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, GP has been extensively utilized for image analysis [7], [19], [41]- [43]. In addition to image analysis, GP has successfully achieved promising results in scheduling, classification, and symbolic regression [44]. Earlier in 2003, Zhang et al proposed an object detection method using GP [19].…”

Section: Measure Formulamentioning

confidence: 99%

Automatically Diagnosing Suspicious Regions in Skin Images Using Two-Stage Genetic Programming

Ain¹,

Al-Sahaf²,

Xue³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Developing a computer-aided diagnostic system for detecting various skin malignancies from images has attracted many researchers. Unlike many machine learning approaches such as Artificial Neural Networks, Genetic Programming (GP) automatically evolves models with flexible representation. GP successfully provides effective solutions using its intrinsic ability to select prominent features (i.e. feature selection) and build new features (i.e. feature construction). Existing approaches have utilized GP to construct new features from the complete set of original features and the set of operators. However, the complete set of features may contain redundant or irrelevant features that do not provide useful information for classification. This study aims to develop a two-stage GP method where the first stage selects prominent features, and the second stage constructs new features from these selected features and operators such as multiplication in a wrapper approach to improve the classification performance. To include local, global, texture, color, and multi-scale image properties of skin images, GP selects and constructs features extracted from local binary patterns and pyramid-structured wavelet decomposition. The accuracy of this GP method is assessed using two real-world skin image datasets captured from the standard camera and specialized instruments, and compared with commonly used classification algorithms, three state-of-the-art, and an existing embedded GP method. The results reveal that this new approach of feature selection and feature construction effectively helps improve the performance of the machine learning classification algorithms. Unlike other black-box models, the evolved models by GP are interpretable, therefore the proposed method can assist dermatologists to identify prominent features, which has been shown by further analysis on the evolved models.

show abstract

Section: Measure Formulamentioning

confidence: 99%

Automatically Diagnosing Suspicious Regions in Skin Images Using Two-Stage Genetic Programming

Ain¹,

Al-Sahaf²,

Xue³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…FS and FC have seen wide application in supervised learning tasks [41,39], including EC-based FS [43] and FC [32,42]. FS and FC have also been used in unsupervised learning tasks [12], but little research has considered EC-based approaches for these tasks [2]. In particular, only a few papers have used EC-based FC for unsupervised learning: mostly in clustering tasks [4,7], but also for the creation of benchmark datasets [38,21].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Lensen

Zhang

Xue

2020

Genet Program Evolvable Mach

Self Cite

View full text Add to dashboard Cite

Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset. In this paper, we substantially extend our previous work, by introducing a multi-objective approach that automatically balances the competing objectives of manifold quality and dimensionality. Our proposed approach is competitive with a range of baseline and state-of-the-art manifold learning methods, while also providing a range (front) of solutions that give different tradeoffs between quality and dimensionality. Furthermore, the learned models are shown to often be simple and efficient, utilising only a small number of features in an interpretable manner.

show abstract

Section: Dimensionality Reductionmentioning

confidence: 99%

“…-Principal Component Analysis (PCA) 2 [17]: computes a number of linearly uncorrelated components, such that each successive component represents the axis of most remaining variance; -MultiDimensional Scaling (MDS) [19]: attempts to maintain the high-dimensional distance between instances in the low-dimensional space; -Locally Linear Embedding (LLE) [37]: models each instance as a linear combination of its high-dimensional nearest neighbours and attempts to maintain this combination in the low-dimensional space; -Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) [29]: models the high-dimensional structure as a fuzzy topological structure, and then attempts to find a low-dimensional embedding that has the closest equivalent fuzzy topological structure. UMAP is often regarded as the state-of-the-art manifold learning technique, and a spiritual successor to the widely-known t-Distributed Stochastic Neighbour Embedding (t-SNE) method [27] that we compared to in our initial work.…”

Section: Experiments Designmentioning

confidence: 99%

Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Lensen¹,

Zhang²,

Xue³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially reduce the dimensionality of a dataset while preserving as much information as possible. However, state-of-the-art manifold learning algorithms are opaque in how they perform this transformation. Understanding the way in which the embedding relates to the original high-dimensional space is critical in exploratory data analysis. We previously proposed a Genetic Programming method that performed manifold learning by evolving mappings that are transparent and interpretable. This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset. In this paper, we substantially extend our previous work, by introducing a multi-objective approach that automatically balances the competing objectives of manifold quality and dimensionality. Our proposed approach is competitive with a range of baseline and state-of-the-art manifold learning methods, while also providing a range (front) of solutions that give different trade-offs between quality and dimensionality. Furthermore, the learned models are shown to often be simple and efficient, utilising only a small number of features in an interpretable manner.

show abstract

A survey on evolutionary machine learning

Cited by 174 publications

References 169 publications

Automatically Diagnosing Suspicious Regions in Skin Images Using Two-Stage Genetic Programming

Automatically Diagnosing Suspicious Regions in Skin Images Using Two-Stage Genetic Programming

Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Multi-objective genetic programming for manifold learning: balancing quality and dimensionality

Contact Info

Product

Resources

About