Hierarchical Feature Selection for Knowledge Discovery

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   • Cen Wan

   Pages 1-6

3. Data Mining Tasks and Paradigms

   • Cen Wan

   Pages 7-15

4. Feature Selection Paradigms

   • Cen Wan

   Pages 17-23

5. Background on Biology of Ageing and Bioinformatics

   • Cen Wan

   Pages 25-43

6. Lazy Hierarchical Feature Selection

   • Cen Wan

   Pages 45-80

7. Eager Hierarchical Feature Selection

   • Cen Wan

   Pages 81-104

8. Comparison of Lazy and Eager Hierarchical Feature Selection Methods and Biological Interpretation on Frequently Selected Gene Ontology Terms Relevant to the Biology of Ageing

   • Cen Wan

   Pages 105-114

9. Conclusions and Research Directions

   • Cen Wan

   Pages 115-117

10. Back Matter

    Pages 119-120

    PDF

This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation providesthe resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

• Bioinformatics
• Hierarchical Feature Selection
• Gene Ontology
• Biology of Ageing
• Data Mining
• Knowledge Discovery

• Department of Computer Science, University College London, London, UK

  Cen Wan

Dr. Cen Wan is a Postdoctoral Research Associate in the Department of Computer Science at University College London, and in the Biomedical Data Science Laboratory at The Francis Crick Institute, London, UK.

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