Results 291 to 300 of about 9,752,550 (347)

Analysis of the prediction performance of decision tree-based algorithms

2023 International Conference on Decision Aid Sciences and Applications (DASA), 2023
Due to their many benefits, decision trees are extensively utilized to solve a variety of classification problems in the real world. Consequently, creating efficient and effective decision trees remains a common challenge for researchers in the fields of
F. Aaboub, Hasna Chamlal, Tayeb Ouaderhman   +2 more
semanticscholar   +1 more source

Trees and decisions [PDF]

Economic Theory, 2005
The traditional model of sequential decision making, for instance, in extensive form games, is a tree. Most texts define a tree as a connected directed graph without loops and a distinguished node, called the root. But an abstract graph is not a domain for decision theory.
Alós-Ferrer, C., Ritzberger, K.
openaire   +4 more sources

A feature selection algorithm of decision tree based on feature weight

Expert systems with applications, 2021
In order to improve the classification accuracy, a preprocessing step is used to pre-filter some redundant or irrelevant features before decision tree construction. And a new feature selection algorithm FWDT is proposed based on this.
Hong-Fang Zhou, Jiawei Zhang, Yue-Qin Zhou, Xiaojie Guo, Yiming Ma   +4 more
semanticscholar   +1 more source

Hybrid decision tree-based machine learning models for short-term water quality prediction.

Chemosphere, 2020
Water resources are the foundation of people's life and economic development, and are closely related to health and the environment. Accurate prediction of water quality is the key to improving water management and pollution control.
Hong-Fang Lu, Xin Ma
semanticscholar   +1 more source

A Pearson’s correlation coefficient based decision tree and its parallel implementation

Information Sciences, 2018
Xiaodong Liu, Yashuang Mu, Lidong Wang
exaly   +2 more sources

Decision Tree for Sequences

IEEE Transactions on Knowledge and Data Engineering, 2021
Current decision trees such as C4.5 and CART are widely used in different fields due to their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers, these tree-based classification algorithms are developed for fixed-length vector data and suffer from intrinsic limitations in handling complex data such as sequences.
Zengyou He, Ziyao Wu, Guangyao Xu, Yan Liu 0085, Quan Zou 0001   +4 more
openaire   +2 more sources

Decision tree methods: applications for classification and prediction

Shanghai Archives of Psychiatry, 2015
Summary Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable.
Yanyan Song, Ying Lu
semanticscholar   +1 more source

Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.

Science of the Total Environment, 2019
Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree ...
Wei Chen, Yang Li, Weifeng Xue, H. Shahabi, Shaojun Li, H. Hong, Xiaojing Wang, Hui-Yuan Bian, Shuai Zhang, B. Pradhan, Baharin Bin Ahmad   +10 more
semanticscholar   +1 more source

A gradient boosting decision tree based GPS signal reception classification algorithm

Applied Soft Computing, 2020
In urban areas, GPS signals are often reflected or blocked by buildings, which causes multipath effects and non-line-of-sight (NLOS) reception respectively consequently degrading GPS positioning performance.
Rui Sun, Guanyu Wang, Wen-Yu Zhang, Li-Ta Hsu, W. Ochieng   +4 more
semanticscholar   +1 more source

Feature selection based on artificial bee colony and gradient boosting decision tree

Applied Soft Computing, 2019
Data from many real-world applications can be high dimensional and features of such data are usually highly redundant. Identifying informative features has become an important step for data mining to not only circumvent the curse of dimensionality but to
H. Rao, Xianzhang Shi, A. Rodrigue, Juanjuan Feng, Yingchun Xia, M. Elhoseny, Xiao-Hui Yuan, Lichuan Gu   +7 more
semanticscholar   +1 more source