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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 Recent advances have shown how decision trees are apt data structures for concisely representing strategies (or controllers) satisfying various objectives. Moreover, they also make the strategy more explainable.
Ashok P +5 more
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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
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Computers and Education: Artificial Intelligence, 2021 In the area of machine learning and data science, decision tree learning is considered as one of the most popular classification techniques. Therefore, a decision tree algorithm generates a classification and predictive model, which is simple to ...
Vasiliki Matzavela, Efthimios Alepis
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Tree retraining in the decision tree learning algorithm
IOP Conference Series: Materials Science and Engineering, 2021 Decision trees belong to the most effective classification methods. The main advantage of decision trees is a simple and user-friendly interpretation of the results obtained. But despite its well-known advantages the method has some disadvantages as well.
S. Mitrofanov, E. Semenkin
semanticscholar +3 more sources
Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging
Frontiers in Medicine, 2020 The global pandemic of coronavirus disease 2019 (COVID-19) has resulted in an increased demand for testing, diagnosis, and treatment. Reverse transcription polymerase chain reaction (RT-PCR) is the definitive test for the diagnosis of COVID-19; however ...
Seung Hoon Yoo +11 more
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Learning decision trees using the Fourier spectrum [PDF]
SIAM Journal on Computing, 1991 Summary: This work gives a polynomial time algorithm for learning decision trees with respect to the uniform distribution. (This algorithm uses membership queries.) The decision tree model that is considered is an extension of the traditional Boolean decision tree model that allows linear operations in each node (i.e., summation of a subset of the ...
Eyal Kushilevitz, Yishay Mansour
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Interval Temporal Logic Decision Tree Learning [PDF]
European Conference on Logics in Artificial Intelligence, 2019 Decision trees are simple, yet powerful, classification models used to classify categorical and numerical data, and, despite their simplicity, they are commonly used in operations research and management, as well as in knowledge mining.
Andrea Brunello +2 more
semanticscholar +6 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
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Learning decision tree classifiers [PDF]
ACM Computing Surveys, 1996 J. R. Quinlan
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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
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