Results 41 to 50 of about 169,825 (305)
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 ...
Olcay Taner Yildiz, Ethem Alpaydin
openaire +2 more sources
CoRR, 2020 Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in some settings DTs can hardly be deemed interpretable, with paths in a DT being arbitrarily larger than a PI ...
Yacine Izza +2 more
openaire +2 more sources
Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison [PDF]
Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021), 2021 In reliability engineering, we need to understand system dependencies, cause-effect relations, identify critical components, and analyze how they trigger failures. Three prominent graph models commonly used for these purposes are fault trees (FTs), decision trees (DTs), and binary decision diagrams (BDDs). These models are popular because they are easy
Jimenez-Roa, L.A. +2 more
openaire +3 more sources
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 +5 more
core +1 more source
Utilizing fuzzy decision trees in decision making
, 2008 The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity.
Beynon, Malcolm James +1 more
core +1 more source
KnowledgeKnowledge 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 +5 more
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 +2 more
doaj +1 more source
Comparing several approaches for hierarchical classification of proteins with decision trees [PDF]
, 2007 Proteins are the main building blocks of the cell, and perform almost all the functions related to cell activity. Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. The use of algorithms able to
Carvalho, Andre C. P. L. F. +4 more
core +1 more source
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 +3 more
doaj +1 more source
, 2008 The inductive learning methodology known as decision trees, concerns the ability to classify objects based on their attributes values, using a tree like structure from which decision rules can be accrued.
Beynon, Malcolm James, Malcolm J. Beynon
core +1 more source