© 2020

Nature-Inspired Computation in Data Mining and Machine Learning

  • Xin-She Yang
  • Xing-Shi He

Part of the Studies in Computational Intelligence book series (SCI, volume 855)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Douglas Rodrigues, Gustavo Henrique de Rosa, Leandro Aparecido Passos, João Paulo Papa
    Pages 1-21
  3. Ratnik Gandhi, Mehul S Raval
    Pages 23-46
  4. Mohamed Alloghani, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Thar Baker, Ahmed J. Aljaaf
    Pages 47-76
  5. Adis Alihodzic, Eva Tuba, Milan Tuba
    Pages 95-112
  6. Mohamed Alloghani, Thar Baker, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Ahmed J. Aljaaf
    Pages 113-136
  7. Janmenjoy Nayak, Bighnaraj Naik, Danilo Pelusi, A. Vamsi Krishna
    Pages 137-159
  8. Nabila Zrira, Mohamed Hannat, El Houssine Bouyakhf
    Pages 161-186
  9. Mohamed Alloghani, Dhiya Al-Jumeily, Abir Hussain, Panagiotis Liatsis, Ahmed J. Aljaaf
    Pages 187-206
  10. Ravinder Ahuja, Aakarsha Chug, Shaurya Gupta, Pratyush Ahuja, Shruti Kohli
    Pages 225-248

About this book


This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details.
Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.


Nature-inspired Algorithm Computational Intelligence Bio-inspired Computation Bioinformatics Data Mining Evolutionary Computing Machine Learning Data Modeling Heuristics Pattern Recognition

Editors and affiliations

  • Xin-She Yang
    • 1
  • Xing-Shi He
    • 2
  1. 1.School of Science and TechnologyMiddlesex UniversityLondonUK
  2. 2.College of ScienceXi’an Polytechnic UniversityXi’anChina

Bibliographic information