Results 1 to 10 of about 1,068,490 (137)
, 1990 This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach.
Chris Carter, Suk Wah Kwok
openaire +4 more sources
Bivariate decision trees [PDF]
, 1997 Decision tree methods constitute an important and much used technique for classification problems. When such trees are used in a Datamining and Knowledge Discovery context, ease of interpretation of the resulting trees is an important requirement to be met.
Rob Potharst +2 more
openaire +4 more sources
Multivariate decision trees [PDF]
Machine Learning, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carla E. Brodley, Paul E. Utgoff
openaire +4 more sources
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
Machine Learning, 1993 Quinlan and Rivest have suggested a decision-tree inference method using the Minimum Description Length idea. We show that there is an error in their derivation of message lengths, which fortunately has no effect on the final inference. We further suggest two improvements to their coding techniques, one removing an inefficiency in the description of ...
Chris S. Wallace, J. D. Patrick
openaire +2 more sources
IEEE Transactions on Knowledge and Data Engineering, 2017 Uncovering causal relationships in data is a major objective of data analytics. Causal relationships are normally discovered with designed experiments, e.g. randomised controlled trials, which, however are expensive or infeasible to be conducted in many cases.
Jiuyong Li +4 more
openaire +4 more sources
Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers [PDF]
, 2015 Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models.
Abdelbar, Ashraf M. +2 more
core +1 more source
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 +4 more sources
Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. [PDF]
, 2020 We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings.
Aboian, Mariam +3 more
core +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