6 Books on Ensemble Learning

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Ensemble studying includes combining the predictions from a number of machine studying fashions.

The impact could be each improved predictive efficiency and decrease variance of the predictions made by the mannequin.

Ensemble strategies are lined in most textbooks on machine studying; however, there are books devoted to the subject.

In this publish, you’ll uncover the highest books on the subject of ensemble machine studying.

After studying this publish, you’ll know:

  • Books on ensemble studying, together with their desk of contents and the place to be taught extra about them.
  • Sections and chapters on ensemble studying in the preferred and customary machine studying textbooks.
  • Book suggestions for machine studying practitioners concerned with ensemble studying.

Let’s get began.

Ensemble Learning Book List

The books devoted to the subject of ensemble studying that we’ll cowl are as follows:

  1. Supervised and Unsupervised Ensemble Methods and their Applications, 2008.
  2. Pattern Classification Using Ensemble Methods, 2010.
  3. Ensemble Learning, 2019.
  4. Ensemble Methods in Data Mining, 2010.
  5. Ensemble Methods, 2012.
  6. Ensemble Machine Learning, 2012.

There are additionally some books from Packt, however I gained’t be reviewing them; they’re:

Did I miss a ebook on ensemble studying?
Let me know within the feedback beneath.

Have you learn any of those books on ensemble studying?
What did you assume? Let me know within the feedback.

Let’s take a more in-depth take a look at these books, together with their writer, desk of contents, and the place to be taught extra.

Supervised and Unsupervised Ensemble Methods and their Applications

The full title of this ebook is “Supervised and Unsupervised Ensemble Methods and their Applications” and it was edited by Oleg Okun and Giorgio Valentini and printed in 2008.

Supervised and Unsupervised Ensemble Methods and their Applications

Supervised and Unsupervised Ensemble Methods and their Applications

This ebook is a set of educational papers by a spread of various authors on the subject of purposes of ensemble studying.

The ebook contains 9 chapters divided into two components, assembling contributions to the purposes of supervised and unsupervised ensembles.

— Page VIII, Supervised and Unsupervised Ensemble Methods and their Applications, 2008.

Table of Contents

  • Part I: Ensembles of Clustering Methods and Their Applications
    • Chapter 01: Cluster Ensemble Methods: From Single Clusterings to Combined Solutions
    • Chapter 02: Random Subspace Ensembles for Clustering Categorical Data
    • Chapter 03: Ensemble Clustering with a Fuzzy Approach
    • Chapter 04: Collaborative Multi-Strategical Clustering for OBject-Oriented Image Analysis
  • Part II: Ensembles of Classification Methods and Their Applications
    • Chapter 05: Intrusion Detection in Computer Systems Using Multiple Classifier Systems
    • Chapter 06: Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification
    • Chapter 07: Multivariate Time Series Classification by way of Stacking of Univariate Classifiers
    • Chapter 08: Gradient Boosting GARCH and Neural Networks for Time Series Prediction
    • Chapter 09: Cascading with VDM and Binary Decision Trees for Nominal Data

I typically wouldn’t advocate this ebook to machine studying practitioners except one of many purposes lined by the ebook is instantly associated to your present challenge.

You can be taught extra about this ebook right here:

Pattern Classification Using Ensemble Methods

The full title of this ebook is “Pattern Classification Using Ensemble Methods” and it was written by Lior Rokach and printed in 2010.

Pattern Classification Using Ensemble Methods

Pattern Classification Using Ensemble Methods

This ebook supplies a technical introduction to the subject of ensemble machine studying written for college kids and teachers.

Throughout the ebook, particular emphasis was put on the intensive use of illustrative examples. Accordingly, along with ensemble principle, the reader can also be supplied with an abundance of synthetic in addition to real-world purposes from a variety of fields. The knowledge referred to on this ebook, in addition to many of the Java implementations of the introduced algorithms, could be obtained by way of the Web.

— Page viii, Pattern Classification Using Ensemble Methods, 2010.

Table of Contents

  • Chapter 01: Introduction to Pattern Classification
  • Chapter 02: Introduction to Ensemble Learning
  • Chapter 03: Ensemble Classification
  • Chapter 04: Ensemble Diversity
  • Chapter 05: Ensemble Selection
  • Chapter 06: Error Correcting Output Codes
  • Chapter 07: Evaluating Ensembles of Classifiers

I like the extent of this ebook. It is technical, however not overly so, and stays grounded within the issues of utilizing ensemble algorithms on supervised predictive modeling initiatives. I believe it’s a good textbook on ensemble studying for practitioners.

You can be taught extra about this ebook right here:

Ensemble Learning

The full title of this ebook is “Ensemble Learning: Pattern Classification Using Ensemble Methods” and it was written by Lior Rokach and printed in 2019.

Ensemble Learning Pattern Classification Using Ensemble Methods

Ensemble Learning Pattern Classification Using Ensemble Methods

This is a direct replace to the ebook “Pattern Classification Using Ensemble Methods” and given a distinct title.

The first version of this ebook was printed a decade in the past. The ebook was well-received by the machine studying and knowledge science communities and was translated into Chinese. […] The second version goals to replace the beforehand introduced materials on the elemental areas, and to current new findings within the area; greater than a 3rd of this version is comprised of latest materials.

— Page vii, Ensemble Learning: Pattern Classification Using Ensemble Methods, 2019.

Table of Contents

  • Chapter 01: Introduction to Machine Learning
  • Chapter 02: Classification and Regression Trees
  • Chapter 03: Introduction to Ensemble Learning
  • Chapter 04: Ensemble Classification
  • Chapter 05: Gradient Boosting Machines
  • Chapter 06: Ensemble Diversity
  • Chapter 07: Ensemble Selection
  • Chapter 08: Error Correcting Output Codes
  • Chapter 09: Evaluating Ensemble Classifiers

This is a good textbook on ensemble studying for college kids and practitioners and is most popular over “Pattern Classification Using Ensemble Methods” for those who should select between the 2.

You can be taught extra about this ebook right here:

Ensemble Methods in Data Mining

The full title of this ebook is “Ensemble Methods in Data Mining: Improving Accuracy
Through Combining Predictions” and it was written by Giovanni Seni and John Elder and printed in 2010.

Ensemble Methods in Data Mining

Ensemble Methods in Data Mining

This is a technical ebook on ensembles, though ideas are demonstrated with full examples in R.

This ebook is geared toward novice and superior analytic researchers and practitioners – particularly in Engineering, Statistics, and Computer Science. Those with little publicity to ensembles will be taught why and tips on how to make use of this breakthrough technique, and superior practitioners will acquire perception into constructing much more highly effective fashions. Throughout, snippets of code in R are offered for example the algorithms described and to encourage the reader to attempt the method.

— Page i, Ensemble Methods in Data Mining, 2010.

Table of Contents

  • Chapter 01: Ensembles Discovered
  • Chapter 02: Predictive Learning and Decision Trees
  • Chapter 03: Model Complexity, Model Selection and Regularization
  • Chapter 04: Importance Sampling and the Classic Ensemble Methods
  • Chapter 05: Rule Ensembles and Interpretation Statistics
  • Chapter 06: Ensemble Complexity
  • Appendix A: AdaBoost Equivalence to FSF Procedure
  • Appendix B: Gradient Boosting and Robust Loss Functions

I imagine that is the primary ebook I bought on ensemble studying years in the past. It is an effective crash course in ensemble studying for practitioners, particularly these already utilizing R. It could also be a bit too mathematical for many practitioners; however, I believe it could be an excellent smaller substitute for the above textbook on ensemble strategies.

You can be taught extra about this ebook right here:

Ensemble Methods

The full title of this ebook is “Ensemble Methods: Foundations and Algorithms” and it was written by Zhi-Hua Zhou and printed in 2012.

Ensemble Methods: Foundations and Algorithms

Ensemble Methods: Foundations and Algorithms

This is one other centered textbook on the subject of ensemble studying focused at college students and teachers.

This ebook supplies researchers, college students and practitioners with an introduction to ensemble strategies. The ebook consists of eight chapters which naturally represent three components.

— Page vii, Ensemble Methods: Foundations and Algorithms, 2012.

Table of Contents

  • Chapter 01: Introduction
  • Chapter 02: Boosting
  • Chapter 03: Bagging
  • Chapter 04: Combination Methods
  • Chapter 05: Diversity
  • Chapter 06: Ensemble Pruning
  • Chapter 07: Clustering Ensembles
  • Chapter 08: Advanced Topics

This ebook is effectively written and covers the principle strategies with good references. I believe it’s one other nice jump-start on the fundamentals of ensemble strategies so long as the reader is snug with a bit math. I appreciated the algorithm descriptions and labored examples.

You can be taught extra about this ebook right here:

Ensemble Machine Learning

The full title of this ebook is “Ensemble Machine Learning: Methods and Applications” and it was edited by Cha Zhang and Yunqian Ma and printed in 2012.

Ensemble Machine Learning

Ensemble Machine Learning

This ebook is a set of educational papers written by a spread of authors on the subject of purposes of ensemble machine studying.

Despite the good success of ensemble studying strategies lately, we discovered only a few books that have been devoted to this matter, and even fewer that offered insights about how such strategies shall be utilized in real-world purposes. The major objective of this ebook is to fill the prevailing hole within the literature and comprehensively cowl the state-of-the-art ensemble studying strategies, and supply a set of purposes that exhibit the varied usages of ensemble studying strategies in the actual world.

— Page v, Ensemble Machine Learning: Methods and Applications, 2012.

Table of Contents

  • Chapter 01: Ensemble Learning
  • Chapter 02: Boosting Algorithms: A Review of Methods, Theory, and Applications
  • Chapter 03: Boosting Kernel Estimators
  • Chapter 04: Targeted Learning
  • Chapter 05: Random Forests
  • Chapter 06: Ensemble Learning by Negative Correlation Learning
  • Chapter 07: Ensemble Nystrom
  • Chapter 08: Object Detection
  • Chapter 09: Classifier Boosting for Human Activity Recognition
  • Chapter 10: Discriminative Learning for Anatomical Structure Detection and Segmentation
  • Chapter 1: Random Forest for Bioinformatics

Like different collections of papers, I might typically not advocate this ebook except you’re an instructional or one of many chapters is instantly associated to your present machine studying challenge. Nevertheless, most of the chapters present a stable and compact introduction to ensemble strategies and tips on how to use them on particular purposes.

You can be taught extra about this ebook right here:

Book Chapters

Many machine studying textbooks have sections on ensemble studying.

In this part, we are going to take a fast tour of a number of the extra well-liked textbooks and the related sections on ensemble studying.

The ebook “An Introduction to Statistical Learning with Applications in R” printed in 2016 supplies a stable introduction to boosting and bagging for determination bushes in chapter 8.

  • Section 8.2: Bagging, RandomForests, Boosting

The ebook “Applied Predictive Modeling” printed in 2013 covers the preferred ensemble algorithms with examples in R, with a spotlight on the ensembles of determination bushes.

  • Chapter 8: Regression Trees and Rule-Based Model
  • Chapter 14: Classification Trees and Rule-Based Models

The ebook “Data Mining: Practical Machine Learning Tools and Techniques” printed in 2016 supplies a chapter devoted to ensemble studying and covers a spread of well-liked strategies, together with boosting, bagging, and stacking.

  • Chapter 12: Ensemble Learning.

The ebook “Machine Learning: A Probabilistic Perspective” printed in 2012 supplies a lot of sections on algorithms that carry out ensembling, in addition to a devoted part on the subject centered on stacking and error-correcting output codes.

  • Section 16.2: Classification and regression bushes (CART)
  • Section 16.4: Boosting
  • Section 16.6: Ensemble studying

The ebook “The Elements of Statistical Learning” printed in 2016 covers the important thing ensemble studying algorithms in addition to the speculation for ensemble studying typically.

  • Chapter 8: Model Inference and Averaging
  • Chapter 10: Boosting and Additive Trees
  • Chapter 15: Random Forests
  • Chapter 16: Ensemble Learning

Did I miss your favourite machine studying textbook that has a bit on ensemble studying?
Let me know within the feedback beneath.

Recommendations

I’ve a duplicate of every of those books as I wish to examine a given matter from a number of views.

If you’re searching for a stable textbook devoted to the subject of ensemble studying, I might advocate one of many following:

A detailed runner-up is “Ensemble Methods in Data Mining” that mixes principle and examples in R.

Also, I like to recommend “Pattern Classification Using Ensemble Methods” for those who can not get your arms on the more moderen “Ensemble Learning: Pattern Classification Using Ensemble Methods“.

Summary

In this publish, you found a collection of books on the subject of ensemble machine studying.

Do you will have any questions?
Ask your questions within the feedback beneath and I’ll do my greatest to reply.

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