[HTML][HTML] A novel bagging C4. 5 algorithm based on wrapper feature selection for supporting wise clinical decision making

SJ Lee, Z Xu, T Li, Y Yang - Journal of biomedical informatics, 2018 - Elsevier
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 …

Regression random machines: An ensemble support vector regression model with free kernel choice

A Ara, M Maia, F Louzada, S Macêdo - Expert Systems with Applications, 2022 - Elsevier
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 …

Data-driven prediction of decannulation probability and timing in patients with severe acquired brain injury

A Mannini, B Hakiki, P Liuzzi, S Campagnini… - Computer Methods and …, 2021 - Elsevier
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 …

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 …

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 …

Intelligent intrusion detection system through combined and optimized machine learning

SAR Shah, B Issac, SM Jacob - International Journal of …, 2018 - World Scientific
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 …

Randomization vs optimization in svm ensembles

M Sabzevari, G Martínez-Muñoz, A Suárez - … 4-7, 2018, Proceedings, Part II …, 2018 - Springer
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 …

SVM Ensembles on a Budget

D Nevado, G Martínez-Muñoz, A Suárez - International Conference on …, 2022 - Springer
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 …

[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 …

[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 …