Results 31 to 40 of about 883,011 (303)

Design of Neuro-Fuzzy Decision Trees

MATEC Web of Conferences, 2016
In order to improve accuracy of fuzzy decision trees classification we propose a procedure of parameters adaptation by means of neural network training.
Abramova Tatyana
doaj   +1 more source

Learning Explainable Decision Rules via Maximum Satisfiability

IEEE Access, 2020
Decision trees are a popular choice for providing explainable machine learning, since they make explicit how different features contribute towards the prediction.
Henrik E. C. Cao, Riku Sarlin, Alexander Jung   +2 more
doaj   +1 more source

Topological Forest

IEEE Access, 2022
We propose a new ML model called Topological Forest that contains an ensemble of decision trees. Unlike a vanilla Random Forest, Topological Forest has a special training process that selects a smaller number of decision trees on a topological graph ...
Murat Ali Bayir, Kiarash Shamsi, Huseyincan Kaynak, Cuneyt Gurcan Akcora   +3 more
doaj   +1 more source

CSNL: A cost-sensitive non-linear decision tree algorithm [PDF]

, 2010
This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision ...
Allwein E. L., Bennett K. P., Bradford J., Breslow L., Brown G., Elkan C., Fan W., Kanani P., Knoll U., Martin A., Masnadi-Shirazi H., Pazzani M., Provost F. J., Sunil Vadera, Ting K., Ting K., Turney P., Vadera S., Vadera S., Zadrozny B., Zhu X.   +20 more
core   +3 more sources

Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation

International Journal of Prognostics and Health Management, 2013
This study presents the methods employed by a team from the department of Mechatronics and Dynamics at the University of Paderborn, Germany for the 2013 PHM data challenge.
James K. Kimotho, Christoph Sondermann-Woelke, Tobias Meyer, Walter Sextro   +3 more
doaj   +1 more source

Temporary staffing services: a data mining perspective [PDF]

, 2012
Research on the temporary staffing industry discusses different topics ranging from workplace safety to the internationalization of temporary labor. However, there is a lack of data mining studies concerning this topic.
D'Haen, Jeroen, Van den Poel, Dirk
core   +1 more source

Omnivariate decision trees [PDF]

IEEE Transactions on Neural Networks, 2001
Univariate decision trees at each decision node consider the value of only one feature leading to axis-aligned splits. In a linear multivariate decision tree, each decision node divides the input space into two with a hyperplane. In a nonlinear multivariate tree, a multilayer perceptron at each node divides the input space arbitrarily, at the expense ...
C.T. Yildiz, Ethem Alpaydin
openaire   +3 more sources

Decision Stream: Cultivating Deep Decision Trees

, 2017
Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time ...
Ignatov, Andrey, Ignatov, Dmitry
core   +1 more source

Prediction of survival probabilities with Bayesian Decision Trees [PDF]

, 2013
Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients
Bailey, Becalick, Bishop, Bouamra, Boyd, Breiman, Chawda, Chipman, Chipman, Clermont, Denison, Dietterich, DiRusso, Domingos, Duda, Green, Hadfield, Hall, Hilden, Hunter, Jaimes, Jakaite, Kilgo, Kilgo, Koshy, Kreke, Krzanowski, Kuncheva, Li, Livia Jakaite, Lunn, Millham, Oakland, Osler, Osler, Patil, Quinlan, Robert, Rogers, Rogers, Schetinin, Schetinin, Schetinin, Schetinin, Silva, Steyerberg, Stojadinovic, Sujin, Vapnik, Vitaly Schetinin, Wojtek J. Krzanowski   +50 more
core   +1 more source

Dynamic Decision Trees

Knowledge
Knowledge comes in various forms: scientific, artistic, legal, and many others. For most non-computer scientists, it is far easier to express their knowledge in text than in programming code.
Joseph Vidal, Spriha Jha, Zhenyuan Liang, Ethan Delgado, Bereket Siraw Deneke, Dennis Shasha   +5 more
doaj   +1 more source