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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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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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Effective Decision Tree Learning [PDF]
International Journal of Computer Applications, 2013 Classification is a data analysis technique. The decision tree is one of the most popular classification algorithms in current use for data mining because it is more interpretable. Training data sets are not error free due to measurement errors in the data collection process. Traditional decision tree classifiers are constructed without considering any
B. Kumara Swamy Achari +2 more
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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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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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Quality Diversity Evolutionary Learning of Decision Trees
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023 Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant paradigm in the field is based on black-box models, typically in the form of (deep) neural networks, these models lack direct interpretability for human users, i.e., their ...
Andrea Ferigo +2 more
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A Monte Carlo Tree Search Approach to Learning Decision Trees [PDF]
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 Comunicació presentada a: 17th IEEE International Conference on Machine Learning and Applications (ICMLA) celebrada del 17 al 20 de 2018 a Orlando, Estats Units.
Nunes, Cecilia +4 more
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