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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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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
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Learning decision tree classifiers [PDF]
ACM Computing Surveys, 1996 J. R. Quinlan
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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
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Discrimination Aware Decision Tree Learning [PDF]
2010 IEEE International Conference on Data Mining, 2010 Recently, the following problem of discrimination aware classification was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B.
Mykola Pechenizkiy +2 more
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Learning stochastic decision trees
, 2021 We give a quasipolynomial-time algorithm for learning stochastic decision trees that is optimally resilient to adversarial noise. Given an $ $-corrupted set of uniform random samples labeled by a size-$s$ stochastic decision tree, our algorithm runs in time $n^{O(\log(s/\varepsilon)/\varepsilon^2)}$ and returns a hypothesis with error within an ...
Blanc, Guy, Lange, Jane, Tan, Li-Yang
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Agnostically learning decision trees [PDF]
Proceedings of the fortieth annual ACM symposium on Theory of computing, 2008 We give a query algorithm for agnostically learning decision trees with respect to the uniform distribution on inputs. Given black-box access to an *arbitrary* binary function f on the n-dimensional hypercube, our algorithm finds a function that agrees with f on almost (within an epsilon fraction) as many inputs as the best size-t decision tree, in ...
Parikshit Gopalan +2 more
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Tree retraining in the decision tree learning algorithm
IOP Conference Series: Materials Science and Engineering, 2021 Abstract 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.
E S Semenkin, S A Mitrofanov
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Learning Decision Trees Recurrently Through Communication
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process.
Alaniz, Stephan +3 more
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On Learning and Testing Decision Tree
, 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$(n,1/ )$ time algorithms with poly$(s,1/ )$ query complexity (independent of $n$) that distinguish between functions that are decision trees of size $s$ from ...
Bshouty, Nader H. +1 more
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