Decision forest: Twenty years of research
L Rokach - Information Fusion, 2016 - Elsevier
A decision tree is a predictive model that recursively partitions the covariate's space into
subspaces such that each subspace constitutes a basis for a different prediction function …
subspaces such that each subspace constitutes a basis for a different prediction function …
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 …
Classification in the presence of label noise: a survey
B Frénay, M Verleysen - IEEE transactions on neural networks …, 2013 - ieeexplore.ieee.org
Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …
consequences. For example, the accuracy of predictions may decrease, whereas the …
Automated phrase mining from massive text corpora
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality
phrases from a text corpus and has various downstream applications including information …
phrases from a text corpus and has various downstream applications including information …
Predictive entropy search for multi-objective bayesian optimization
D Hernández-Lobato… - International …, 2016 - proceedings.mlr.press
We present\small PESMO, a Bayesian method for identifying the Pareto set of multi-objective
optimization problems, when the functions are expensive to evaluate.\small PESMO …
optimization problems, when the functions are expensive to evaluate.\small PESMO …
[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 …
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
In this paper, we investigate the performance of several systems based on ensemble of
classifiers for bankruptcy prediction and credit scoring. The obtained results are very …
classifiers for bankruptcy prediction and credit scoring. The obtained results are very …
Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors
Abstract The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented
a unique opportunity to the activity-recognition community to test their approaches on a …
a unique opportunity to the activity-recognition community to test their approaches on a …
Strength in numbers: Improving generalization with ensembles in machine learning-based profiled side-channel analysis
The adoption of deep neural networks for profiled side-channel attacks provides powerful
options for leakage detection and key retrieval of secure products. When training a neural …
options for leakage detection and key retrieval of secure products. When training a neural …