Results 41 to 50 of about 1,307,990 (175)

Efficient algorithms for decision tree cross-validation

, 2001
Cross-validation is a useful and generally applicable technique often employed in machine learning, including decision tree induction. An important disadvantage of straightforward implementation of the technique is its computational overhead.
Blockeel, Hendrik, Struyf, Jan
core   +4 more sources

Decision tree-based Design Defects Detection

IEEE Access, 2021
Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on ...
Mohamed Maddeh, Sarra Ayouni, Sultan Alyahya, Fahima Hajjej   +3 more
doaj   +1 more source

Very Fast C4.5 Decision Tree Algorithm

Applied Artificial Intelligence, 2018
This paper presents a novel algorithm so-called VFC4.5 for building decision trees. It proposes an adaptation of the way C4.5 finds the threshold of a continuous attribute. Instead of finding the threshold that maximizes gain ratio, the paper proposes to
Anis Cherfi, Kaouther Nouira, Ahmed Ferchichi   +2 more
doaj   +1 more source

Minimum Query Set for Decision Tree Construction

Entropy, 2021
A new two-stage method for the construction of a decision tree is developed. The first stage is based on the definition of a minimum query set, which is the smallest set of attribute-value pairs for which any two objects can be distinguished.
Wojciech Wieczorek, Jan Kozak, Łukasz Strąk, Arkadiusz Nowakowski   +3 more
doaj   +1 more source

Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles

Entropy
This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments.
Evans Teiko Tetteh, Beata Zielosko
doaj   +1 more source

A Decision Tree for Rockburst Conditions Prediction

Applied Sciences, 2023
This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a ...
Dominic Owusu-Ansah, Joaquim Tinoco, Faramarzi Lohrasb, Francisco Martins, José Matos   +4 more
doaj   +1 more source

Rockburst prediction in kimberlite using decision tree with incomplete data

Journal of Sustainable Mining, 2018
A rockburst is a common engineering geological hazard. In order to predict rockburst potential in kimberlite at an underground diamond mine, a decision tree method was employed.
Yuanyuan Pu, Derek B. Apel, Bob Lingga
doaj   +1 more source

Extension of Decision Tree Algorithm for Stream Data Mining Using Real Data [PDF]

, 2009
Recently, because of increasing amount of data in the society, data stream mining targeting large scale data has attracted attention. The data mining is a technology of discovery new knowledge and patterns from the massive amounts of data, and what the ...
Ise, Masayuki, Konishi, Osamu, Minegishi, Tatsuya, Niimi, Ayahiko   +3 more
core  

P2P Domain Classification using Decision Tree

, 2011
In Peer-to-Peer context, a challenging problem is how to find the appropriate peer to deal with a given query without overly consuming bandwidth? Different methods proposed routing strategies of queries taking into account the P2P network at hand.
Barbar, Aziz, Ismail, Anis
core   +1 more source

Hyperparameter Optimization Using Iterative Decision Tree (IDT)

IEEE Access, 2022
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, Satoshi Sugiura, Mongkut Piantanakulchai   +2 more
doaj   +1 more source