A review on the combination of binary classifiers in multiclass problems
Several real problems involve the classification of data into categories or classes. Given a
data set containing data whose classes are known, Machine Learning algorithms can be …
data set containing data whose classes are known, Machine Learning algorithms can be …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
Human digital twin for fitness management
Our research work describes a team of human Digital Twins (DTs), each tracking fitness-
related measurements describing an athlete's behavior in consecutive days (eg food …
related measurements describing an athlete's behavior in consecutive days (eg food …
[图书][B] Ensemble methods: foundations and algorithms
ZH Zhou - 2012 - books.google.com
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach,
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …
[图书][B] Combining pattern classifiers: methods and algorithms
LI Kuncheva - 2014 - books.google.com
A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …
pattern recognition to ensemble feature selection, now in its second edition The art and …
[图书][B] Pattern classification using ensemble methods
L Rokach - 2010 - books.google.com
1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms.
1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction …
1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction …
[图书][B] Ensemble learning: pattern classification using ensemble methods
L Rokach - 2019 - World Scientific
Artificial intelligence (AI) is a scientific discipline that aims to create intelligent machines.
Machine learning is a popular and practical AI subfield that aims to automatically improve …
Machine learning is a popular and practical AI subfield that aims to automatically improve …
Multiclass from binary: Expanding one-versus-all, one-versus-one and ecoc-based approaches
A Rocha, SK Goldenstein - IEEE transactions on neural …, 2013 - ieeexplore.ieee.org
Recently, there has been a lot of success in the development of effective binary classifiers.
Although many statistical classification techniques have natural multiclass extensions, some …
Although many statistical classification techniques have natural multiclass extensions, some …
On the decoding process in ternary error-correcting output codes
A common way to model multiclass classification problems is to design a set of binary
classifiers and to combine them. Error-correcting output codes (ECOC) represent a …
classifiers and to combine them. Error-correcting output codes (ECOC) represent a …
Clustering-based ensembles for one-class classification
This paper presents a novel multi-class classifier based on weighted one-class support
vector machines (OCSVM) operating in the clustered feature space. We show that splitting …
vector machines (OCSVM) operating in the clustered feature space. We show that splitting …