Results 21 to 30 of about 1,169,808 (377)
Heat demand prediction: A real-life data model vs simulated data model comparison
Energy Reports, 2021 In the recent years machine learning algorithms have developed further and various applications are taking advantage of this advancement. Modern machine learning is now used in district heating for more precise and realistic heat demand prediction ...
Kevin Naik, Anton Ianakiev
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Optimization of decision trees using modified African buffalo algorithm
Journal of King Saud University: Computer and Information Sciences, 2022 Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances.
Archana R. Panhalkar, Dharmpal D. Doye
doaj +1 more source
IEEE Access, 2023 Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a comprehensive review of intrusion detection techniques, and commonly ...
Zahedi Azam +2 more
semanticscholar +1 more source
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
openaire +3 more sources
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
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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TNT: An Interpretable Tree-Network-Tree Learning Framework using Knowledge Distillation
Entropy, 2020 Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case.
Jiawei Li +5 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 Laws of Thought—A Quantum-like Machine Learning Approach
Entropy, 2023 Incorporating insights from quantum theory, we propose a machine learning-based decision-making model, including a logic tree and a value tree; a genetic programming algorithm is applied to optimize both the logic tree and value tree.
Lizhi Xin, Kevin Xin, Houwen Xin
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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
openaire +6 more sources