Results 31 to 40 of about 79,652 (190)

Compilation of novel and renewed, goal oriented digital soil maps using geostatistical and data mining tools

Hungarian Geographical Bulletin, 2015
Due to former soil surveys and mapping activities significant amount of soil information has accumulated in Hungary. Present soil data requirements are mainly fulfilled with these available datasets either by their direct usage or after certain specific ...
László Pásztor, Annamária Laborczi, Katalin Takács, Gábor Szatmári, Endre Dobos, Gábor Illés, Zsófia Bakacsi, József Szabó   +7 more
doaj   +1 more source

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference [PDF]

, 2009
This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection ...
Yang, Bo-Suk, Tan, Andy Chit Chiow, Tran, Van, Tran, Van Tung, Oh, Myung-Suck, Tan, Andy   +5 more
core   +1 more source

evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R

Journal of Statistical Software, 2014
Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search.
Thomas Grubinger, Achim Zeileis, Karl-Peter Pfeiffer   +2 more
doaj   +1 more source

Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients

BMC Medical Informatics and Decision Making, 2023
Background Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge
Koen Welvaars, Michel P. J. van den Bekerom, Job N. Doornberg, Ernst P. van Haarst, OLVG Urology Consortium   +4 more
doaj   +1 more source

Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection

Entropy, 2022
Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically,
Afek Ilay Adler, Amichai Painsky
doaj   +1 more source

Data-driven approach to machine condition prognosis using least square regression trees [PDF]

, 2009
Machine fault prognosis techniques have been profoundly considered in the recent time due to their substantial profit for reducing unexpected faults or unscheduled maintenance.
Yang, Bo-Suk, Tran, Van Tung, Van Tung Tran, Bo-Suk Yang   +3 more
core   +1 more source

An Empirical Study on Predicting Blood Pressure Using Classification and Regression Trees

IEEE Access, 2018
Blood pressure (BP) diseases have become one of the major threats to human health. Continuous measurement of BP has proven to be a prerequisite for effective incident prevention.
Bing Zhang, Zhiyao Wei, Jiadong Ren, Yongqiang Cheng, Zhangqi Zheng   +4 more
doaj   +1 more source

A Method to Build Classification and Regression Trees [PDF]

, 2014
An adaptable structure to build a classification tree is presented. From such structure different existing classification trees can be obtained, but also we can build new ones, and compare the results of different trees (classification error, tree size, number of levels or other defined criteria). We use the adaptable scheme to emulate ID3, C4.5 and M5
Emilio Unda-Trillas, Jorge Rivera-Rovelo
openaire   +2 more sources

Macular Telangiectasia Type 2

Ophthalmology Science, 2023
Purpose: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging. Design: An algorithm was used on data from a prospective natural history study of MacTel for classification development.
Emily Y. Chew, MD, Tunde Peto, MD, PhD, Traci E. Clemons, PhD, Ferenc B. Sallo, MD, PhD, Daniel Pauleikhoff, MD, PhD, Irene Leung, BA, Glenn J. Jaffe, MD, Tjebo F.C. Heeren, MD, Catherine A. Egan, MD, Peter Charbel Issa, MD, PhD, Konstantinos Balaskas, MD, Frank G. Holz, MD, Alain Gaudric, MD, Alan C. Bird, MD, Martin Friedlander, MD, PhD   +14 more
doaj   +1 more source

Does segmentation always improve model performance in credit scoring?

, 2012
Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing
Lyn C. Thomas, Thomas, Lyn C., Katarzyna Bijak, Bijak, Katarzyna   +3 more
core   +1 more source