Results 61 to 70 of about 524,166 (313)
Learning Optimal Decision Trees with SAT [PDF]
Explanations of machine learning (ML) predictions are of fundamental importance in different settings. Moreover, explanations should be succinct, to enable easy understanding by humans. Decision trees represent an often used approach for developing explainable ML models, motivated by the natural mapping between decision tree paths and rules.
Nina Narodytska+3 more
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Optimization of decision trees using modified African buffalo algorithm
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
In the area of machine learning and data science, decision tree learning is considered as one of the most popular classification techniques. Therefore, a decision tree algorithm generates a classification and predictive model, which is simple to ...
Vasiliki Matzavela, Efthimios Alepis
doaj
Deep Learning to Forecast Solar Irradiance Using a Six-Month UTSA SkyImager Dataset
Distributed PV power generation necessitates both intra-hour and day-ahead forecasting of solar irradiance. The UTSA SkyImager is an inexpensive all-sky imaging system built using a Raspberry Pi computer with camera.
Ariana Moncada+2 more
doaj +1 more source
Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis
High rates of inappropriate use of surgical antimicrobial prophylaxis were reported in many countries. Auditing the prophylactic antimicrobial use in enormous medical records by manual review is labor-intensive and time-consuming.
Zhi-Yuan Shi+4 more
doaj +1 more source
Tree in Tree: from Decision Trees to Decision Graphs [PDF]
Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional decision tree to a more generic and powerful directed acyclic graph.
arxiv
Hyperparameter Optimization Using Iterative Decision Tree (IDT)
Machine learning and deep learning have gained a lot of attention from researchers because of their promising predictive performance and the availability of extensive high-dimensional data and high-performance computational hardware.
Narith Saum+2 more
doaj +1 more source
On Decision Trees, Influences, and Learning Monotone Decision Trees
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
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End-to-End Learning of Deterministic Decision Trees [PDF]
Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scratch those features that best ...
Fred A. Hamprecht, Thomas M. Hehn
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Global Evaluation for Decision Tree Learning
We transfer distances on clusterings to the building process of decision trees, and as a consequence extend the classical ID3 algorithm to perform modifications based on the global distance of the tree to the ground truth--instead of considering single leaves.
Spaeh, Fabian, Kosub, Sven
openaire +2 more sources