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Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Background and purpose For postoperative breast cancer patients, deformable image registration (DIR) is challenged due to the large deformations and non‐correspondence caused by tumor resection and clip insertion. To deal with it, three metrics (fiducial‐, region‐, and intensity‐based) were jointly used in DIR algorithm for improved accuracy ...
Xin Xie +6 more
wiley +1 more source
Pembentukan Model Pohon Keputusan pada Database Car Evaluation Menggunakan Statistik Chi-Square
Contemporary Mathematics and Applications (ConMathA), 2022 The study discusses problems related to the formation of a decision tree based on a collection of evaluation data records obtained from a number of car buyers. This secondary data was obtained from the UCL machine learning website.
Retno Maharesi
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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
openaire +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 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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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
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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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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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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Robust algorithm to learn rules for classification: A fault diagnosis case study [PDF]
FME Transactions, 2023 Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning.
Balaji Arun P., Sugumaran V.
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