Results 11 to 20 of about 686,580 (270)

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
Pranav Ashok, Mathias Jackermeier, Jan Křetínský, Christoph Weinhuber, Maximilian Weininger, Mayank Yadav   +5 more
openalex   +2 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, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +1 more source

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, V. Vasu, C. SudarsanaReddy   +2 more
openaire   +1 more source

Decision Tree Learning untuk Penentuan Jalur Kelulusan Mahasiswa [PDF]

, 2016
Jalur kelulusan merupakan hal penentu seorang mahasiswa untuk memperoleh gelar jenjang pendidikan strata satu pada sebuah Perguruan Tinggi. Penelitian ini bertujuan untuk mengetahui pemanfaatan algoritma ID3 dalam penentu jalur kelulusan serta ...
Ariestya, W. W. (Winda), Praptiningsih, Y. E. (Yulia), Supriatin, W. (Wahyu)   +2 more
core   +1 more source

Top-Down Induction of Decision Trees: Rigorous Guarantees and Inherent Limitations [PDF]

, 2019
Consider the following heuristic for building a decision tree for a function $f : \{0,1\}^n \to \{\pm 1\}$. Place the most influential variable $x_i$ of $f$ at the root, and recurse on the subfunctions $f_{x_i=0}$ and $f_{x_i=1}$ on the left and right ...
Blanc, Guy, Lange, Jane, Tan, Li-Yang
core   +2 more sources

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, Leonardo Lucio Custode, Giovanni Iacca   +2 more
openaire   +3 more sources

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, De Craene, Mathieu, Langet, Helene, Camara, Oscar, Jonsson, Anders   +4 more
openaire   +3 more sources

Inducing safer oblique trees without costs [PDF]

, 2005
Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly ...
Althoff K., Bennett K.P., Bennett K.P., Berry M., Blake C., Bradford J., Breiman L., Cohen R., Domingos P., Elkan C., Elomaa T., Fan W., Grefenstette J., Knoll U., Kolodner J., Morrison D., Norusis M., Nunez M., Pazzani M., Provost F.J., Provost F.J., Quinlan J.R., Quinlan J.R., Sunil Vadera, Tan M., Ting K., Turney P., Vadera S.   +27 more
core   +2 more sources

Decision Tree Learning for Uncertain Clinical Measurements [PDF]

IEEE Transactions on Knowledge and Data Engineering, 2021
Clinical decision requires reasoning in the presence of imperfect data. DTs are a well-known decision support tool, owing to their interpretability, fundamental in safety-critical contexts such as medical diagnosis. However, learning DTs from uncertain data leads to poor generalization, and generating predictions for uncertain data hinders prediction ...
Cecilia Nunes, Helene Langet, Mathieu De Craene, Oscar Camara, Bart Bijnens, Anders Jonsson   +5 more
openaire   +3 more sources

On Decision Trees, Influences, and Learning Monotone Decision Trees

, 2004
In this note we prove that a monotone boolean function computable by a decision tree of size s has average sensitivity at most √ log2 s. As a consequence we show that monotone functions are learnable to constant accuracy under the uniform distribution in time polynomial in their decision tree size.
O'Donnell, Ryan, Servedio, Rocco Anthony
openaire   +3 more sources