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Evolutionary Feature Manipulation in Unsupervised Learning

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posted on 2021-12-08, 08:54 authored by Andrew LensenAndrew Lensen

Unsupervised learning is a fundamental category of machine learning that works on data for which no pre-existing labels are available. Unlike in supervised learning, which has such labels, methods that perform unsupervised learning must discover intrinsic patterns within data.  The size and complexity of data has increased substantially in recent years, which has necessitated the creation of new techniques for reducing the complexity and dimensionality of data in order to allow humans to understand the knowledge contained within data. This is particularly problematic in unsupervised learning, as the number of possible patterns in a dataset grows exponentially with regard to the number of dimensions. Feature manipulation techniques such as feature selection (FS) and feature construction (FC) are often used in these situations. FS automatically selects the most valuable features (attributes) in a dataset, whereas FC constructs new, more powerful and meaningful features that provide a lower-dimensional space.  Evolutionary computation (EC) approaches have become increasingly recognised for their potential to provide high-quality solutions to data mining problems in a reasonable amount of computational time. Unlike other popular techniques such as neural networks, EC methods have global search ability without needing gradient information, which makes them much more flexible and applicable to a wider range of problems. EC approaches have shown significant potential in feature manipulation tasks with methods such as Particle Swarm Optimisation (PSO) commonly used for FS, and Genetic Programming (GP) for FC.  The use of EC for feature manipulation has, until now, been predominantly restricted to supervised learning problems. This is a notable gap in the research: if unsupervised learning is even more sensitive to high-dimensionality, then why is EC-based feature manipulation not used for unsupervised learning problems?  This thesis provides the first comprehensive investigation into the use of evolutionary feature manipulation for unsupervised learning tasks. It clearly shows the ability of evolutionary feature manipulation to improve both the performance of algorithms and interpretability of solutions in unsupervised learning tasks. A variety of tasks are investigated, including the well-established task of clustering, as well as more recent unsupervised learning problems, such as benchmark dataset creation and manifold learning.  This thesis proposes a new PSO-based approach to performing simultaneous FS and clustering. A number of improvements to the state-of-the-art are made, including the introduction of a new medoid-based representation and an improved fitness function. A sophisticated three-stage algorithm, which takes advantage of heuristic techniques to determine the number of clusters and to fine-tune clustering performance is also developed. Empirical evaluation on a range of clustering problems demonstrates a decrease in the number of features used, while also improving the clustering performance.  This thesis also introduces two innovative approaches to performing wrapper-based FC in clustering tasks using GP. An initial approach where constructed features are directly provided to the k-means clustering algorithm demonstrates the clear strength of GP-based FC for improving clustering results. A more advanced method is proposed that utilises the functional nature of GP-based FC to evolve more specific, concise, and understandable similarity functions for use in clustering algorithms. These similarity functions provide clear improvements in performance and can be easily interpreted by machine learning practitioners.  This thesis demonstrates the ability of evolutionary feature manipulation to solve unsupervised learning tasks that traditional methods have struggled with. The synthesis of benchmark datasets has long been a technique used for evaluating machine learning techniques, but this research is the first to present an approach that automatically creates diverse and challenging redundant features for a given dataset. This thesis introduces a GP-based FC approach that creates difficult benchmark datasets for evaluating FS algorithms. It also makes the intriguing discovery that using a mutual information-based fitness function with GP has the potential to be used to improve supervised learning tasks even when the labels are not utilised.  Manifold learning is an approach to dimensionality reduction that aims to reduce dimensionality by discovering the inherent lower-dimensional structure of a dataset. While state-of-the-art manifold learning approaches show impressive performance in reducing data dimensionality, they do so at the cost of removing the ability for humans to understand the data in terms of the original features. By utilising a GP-based approach, this thesis proposes new methods that can perform interpretable manifold learning, which provides deep insight into patterns in the data.  These four contributions clearly support the hypothesis that evolutionary feature manipulation has untapped potential in unsupervised learning. This thesis demonstrates that EC-based feature manipulation can be successfully applied to a variety of unsupervised learning tasks with clear improvements in both performance and interpretability. A plethora of future research directions in this area are also discovered, which we hope will lead to further valuable findings in this area.


Copyright Date


Date of Award



Te Herenga Waka—Victoria University of Wellington

Rights License

Author Retains Copyright

Degree Discipline

Computer Science

Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level


Degree Name

Doctor of Philosophy

ANZSRC Type Of Activity code

970108 Expanding Knowledge in the Information and Computing Sciences

Victoria University of Wellington Item Type

Awarded Doctoral Thesis



Victoria University of Wellington School

School of Engineering and Computer Science


Zhang, Mengjie; Xue, Bing