Results 31 to 40 of about 686,580 (270)

Global Evaluation for Decision Tree Learning

, 2022
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

Lower bounds on learning decision lists and trees

Information and Computation, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ming Li, John Tromp, Thomas R. Hancock, Tao Jiang   +3 more
openaire   +4 more sources

Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case‐control study

Molecular Oncology, EarlyView.
This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima, Yuko Takami, Ken Kawabe, Kazuhiro Tanabe, Chihiro Hayashi, Mikio Mikami, Tetsuya Kusumoto   +6 more
wiley   +1 more source

Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers [PDF]

, 2015
Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models.
Abdelbar, Ashraf M., Otero, Fernando E.B., Salama, Khalid M.   +2 more
core   +1 more source

An application of decision trees method for fault diagnosis of induction motors [PDF]

, 2006
Decision tree is one of the most effective and widely used methods for building classification model. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the decision tree ...
Oh, Myung-Suck, Tran, Van Tung, Yang, Bo-Suk   +2 more
core  

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Learning-Based Synthesis of Safety Controllers

, 2019
We propose a machine learning framework to synthesize reactive controllers for systems whose interactions with their adversarial environment are modeled by infinite-duration, two-player games over (potentially) infinite graphs.
beyene, brázdil, de moura, flanagan, garg, grädel, neider, neider, quinlan, solar-lezama   +9 more
core   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

Multi-test Decision Tree and its Application to Microarray Data Classification [PDF]

, 2014
Objective: The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the ...
Armstrong, Berzal, Breiman, Breiman, Breiman, Brodley, Brown, Brown, Che, Chen, Cohen, Cordell, Cowell, Czajkowski, Demsar, Dettling, Diaz-Uriarte, Dramiński, Fayyad, Freund, Freund, Ge, Golub, Grześ, Hall, Hastie, Hu, Kuo, Li, Marcin Czajkowski, Marek Grześ, Marek Kretowski, Murthy, Murthy, Pagallo, Qu, Quinlan, Robnik-Siikonja, Rokach, Rokach, Sebastiani, Shalev-Shwartz, Shi, Tan, Tan, Wold, Yeoh   +46 more
core   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source