Results 21 to 30 of about 5,778,654 (336)
A Bi-criteria optimization model for adjusting the decision tree parameters
Kuwait Journal of Science, 2022 Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree ...
Mohammad Azad, Mikhail Moshkov
doaj +1 more source
, 2022 Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly trained with backpropagation, like a neural network.
Ozan İrsoy, Ethem Alpaydın
openaire +3 more sources
Optimization of decision trees using modified African buffalo algorithm
Journal of King Saud University: Computer and Information Sciences, 2022 Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances.
Archana R. Panhalkar, Dharmpal D. Doye
doaj +1 more source
Tree in Tree: from Decision Trees to Decision Graphs
, 2021 Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph.
Zhu, Bingzhao, Shoaran, Mahsa
openaire +2 more sources
, 2015 Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes.
Frank, Eibe +2 more
core +2 more sources
FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees [PDF]
Judgment and Decision Making, 2017 Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily ...
Nathaniel D. Phillips +3 more
doaj +3 more sources
DECISION TREES BASED ON MEMRISTOR TECHNOLOGY
Надежность и качество сложных систем, 2022 Background. Despite significant progress in neuroscience recently, understanding of the principles and mechanisms underlying complex brain functions and cognition remains incomplete.
A.Yu. Dorosinskiy +3 more
doaj +1 more source
Improved Random Forest Algorithm Based on Out-of-Bag Prediction and Extended Space [PDF]
Jisuanji gongcheng, 2022 On the basis of the bootstrap method, the random forest algorithm constructs a decision tree by using sampling characteristics.This reduces the correlation among decision trees at the expense of decision tree accuracy, thereby improving the prediction ...
CHANG Shuo, ZHANG Yanchun
doaj +1 more source
Practical Federated Gradient Boosting Decision Trees [PDF]
AAAI Conference on Artificial Intelligence, 2019 Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting.
Q. Li, Zeyi Wen, Bingsheng He
semanticscholar +1 more source
Caspian Journal of Neurological Sciences, 2021 Background: Epilepsy is a brain disorder that changes the basin geometry of the oscillation of trajectories in the phase space. Nevertheless, recent studies on epilepsy often used the statistical characteristics of this space to diagnose epileptic ...
Reyhaneh Zarifiyan Irani Nezhad +4 more
doaj +1 more source