[HTML][HTML] A novel bagging C4. 5 algorithm based on wrapper feature selection for supporting wise clinical decision making
From the perspective of clinical decision-making in a Medical IoT-based healthcare system,
achieving effective and efficient analysis of long-term health data for supporting wise clinical …
achieving effective and efficient analysis of long-term health data for supporting wise clinical …
Regression random machines: An ensemble support vector regression model with free kernel choice
Abstract Machine learning techniques have one of their main objectives to reduce the
generalized prediction error. Support vector models have as a main challenge the choice of …
generalized prediction error. Support vector models have as a main challenge the choice of …
Data-driven prediction of decannulation probability and timing in patients with severe acquired brain injury
Background and objectives From a rehabilitation perspective, removal of tracheostomy in
patients with severe acquired brain injuries (sABI) is a crucial step. Predictive parameters for …
patients with severe acquired brain injuries (sABI) is a crucial step. Predictive parameters for …
Synergy of monotonic rules
V Vapnik, R Izmailov - Journal of Machine Learning Research, 2016 - jmlr.org
This article describes a method for constructing a special rule (we call it synergy rule) that
uses as its input information the outputs (scores) of several monotonic rules which solve the …
uses as its input information the outputs (scores) of several monotonic rules which solve the …
Hierarchical ant colony for simultaneous classifier selection and hyperparameter optimization
VO Costa, CR Rodrigues - 2018 IEEE congress on evolutionary …, 2018 - ieeexplore.ieee.org
To simultaneously perform model selection and hyperparameter optimization without human
intervention, a hierarchical problem by nature, is the aim of the expanding area of …
intervention, a hierarchical problem by nature, is the aim of the expanding area of …
Intelligent intrusion detection system through combined and optimized machine learning
In this paper, an existing rule-based intrusion detection system (IDS) is made more
intelligent through the application of machine learning. Snort was chosen as it is an open …
intelligent through the application of machine learning. Snort was chosen as it is an open …
Randomization vs optimization in svm ensembles
Ensembles of SVMs are notoriously difficult to build because of the stability of the model
provided by a single SVM. The application of standard bagging or boosting algorithms …
provided by a single SVM. The application of standard bagging or boosting algorithms …
SVM Ensembles on a Budget
This paper presents a model to train an ensemble of SVMs that achieves better
generalization performance at a lower computational training cost than a single SVM. The …
generalization performance at a lower computational training cost than a single SVM. The …
[HTML][HTML] Subsampling strategies in svm ensembles
P Koch, W Konen - Proceedings 23. Workshop Computational …, 2014 - books.google.com
Abstract Support Vector Machines (SVMs) have shown to be strong methods for
classification problems. Especially for difficult tasks the performance of SVMs is often …
classification problems. Especially for difficult tasks the performance of SVMs is often …
[PDF][PDF] The TDMR 2.2 Tutorial: Examples for Tuned Data Mining in R
W Konen, P Koch - 2016 - gm.th-koeln.de
The TDMR framework is written in R with the aim to facilitate the training, tuning and
evaluation of data mining (DM) models. It puts special emphasis on tuning these data mining …
evaluation of data mining (DM) models. It puts special emphasis on tuning these data mining …