Results 11 to 20 of about 524,166 (313)
A Reinforcement Learning Approach to Online Learning of Decision Trees [PDF]
arXiv, 2015 Online decision tree learning algorithms typically examine all features of a new data point to update model parameters. We propose a novel alternative, Reinforcement Learning- based Decision Trees (RLDT), that uses Reinforcement Learning (RL) to actively examine a minimal number of features of a data point to classify it with high accuracy. Furthermore,
Abhinav Garlapati +3 more
arxiv +3 more sources
Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings [PDF]
Frontiers in Oncology, 2020 We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings.
Seyedmehdi Payabvash +5 more
doaj +2 more sources
On Learning and Testing Decision Tree [PDF]
arXiv, 2021 In this paper, we study learning and testing decision tree of size and depth that are significantly smaller than the number of attributes $n$. Our main result addresses the problem of poly$(n,1/\epsilon)$ time algorithms with poly$(s,1/\epsilon)$ query complexity (independent of $n$) that distinguish between functions that are decision trees of size $
Bshouty, Nader H. +1 more
arxiv +3 more sources
Eduser, 2019 In this paper, we present a real in-streaming case of learning about how to program with the z-tree software to design experiments on economic decision making for the members of the NECE Research Unit in Business Sciences, in Portugal.This
Nuria Hernández-León +2 more
doaj +3 more sources
Learning decision tree classifiers [PDF]
ACM Computing Surveys, 1996 J. R. Quinlan
openalex +3 more sources
dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts [PDF]
Tools and Algorithms for the Construction and Analysis of Systems27th International Conference, 2021 Ashok P +5 more
europepmc +3 more sources
Learning accurate and interpretable decision trees [PDF]
arXivDecision trees are a popular tool in machine learning and yield easy-to-understand models. Several techniques have been proposed in the literature for learning a decision tree classifier, with different techniques working well for data from different domains. In this work, we develop approaches to design decision tree learning algorithms given repeated
Balcan, Maria-Florina, Sharma, Dravyansh
arxiv +3 more sources
Learning with continuous piecewise linear decision trees [PDF]
Expert Systems with Applications, 2020 Abstract In this paper, we propose a piecewise linear decision tree and its generalized form, namely the (G)PWL-DT, which introduces piecewise linearity and overcomes the discontinuity of the existing piecewise constant decision trees (PWC-DT). The proposed (G)PWL-DT inherits the basic topology and interpretability of decision trees by recursively ...
Qinghua Tao +5 more
openalex +4 more sources
Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains
Entropy, 2023 Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data.
Moxuan Fu +5 more
doaj +1 more source
Evolutionary Learning of Interpretable Decision Trees
IEEE Access, 2023 69 pages, 31 figures, code available at: https://gitlab.com/leocus ...
Custode, Leonardo Lucio, Iacca, Giovanni
openaire +4 more sources