Building fast decision trees from large training sets

Franco-Arcega, Anilu; Carrasco-Ochoa, Jesús Ariel; Sánchez-Díaz, Guillermo; Martínez-Trinidad, José Fco.

doi:10.3233/ida-2012-0542

Cited by 18 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concepts of AI and machine learning were proposed at the Dartmouth Conference in 1956, aim at enabling machines to learn rules from historical data and then apply them in the future (Simon 1983 ). After years of development and improvement, many machine learning algorithms have been developed, including linear regression, nearest neighbor (Hart 1968 ), logical regression (Menard 2004 ), decision tree (Franco-Árcega et al 2012 ), random forest (Cutler et al 2012 ), Bayesian (Feng 2010 ), clustering algorithms (Havens et al 2012 ), and support vector machines (Saunders et al 2002 ). Deep learning is a branch of machine learning and incorporates the idea of artificial neural networks (McCulloch and Pitts 1943 ), which aims to reduce human factors and stimulate the intellectual ability of the system.…”

Section: New Technologies Applied In Idpmimentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

View full text Add to dashboard Cite

Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

show abstract

Section: New Technologies Applied In Idpmimentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

View full text Add to dashboard Cite

show abstract

“…Hulten designed CVFDT algorithm, which extended VFDT and required users to give parameters in advance [8]. Recently, some researchers have also proposed many other decision tree algorithms based on information entropy [9][10][11][12][13][14][15][16].…”

Section: Information Entropy Decisionmentioning

confidence: 99%

Random Fuzzy Granular Decision Tree

Chen

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this study, the classification problem is solved from the view of granular computing. That is, the classification problem is equivalently transformed into the fuzzy granular space to solve. Most classification algorithms are only adopted to handle numerical data; random fuzzy granular decision tree (RFGDT) can handle not only numerical data but also nonnumerical data like information granules. Measures can be taken in four ways as follows. First, an adaptive global random clustering (AGRC) algorithm is proposed, which can adaptively find the optimal cluster centers and maximize the ratio of interclass standard deviation to intraclass standard deviation, and avoid falling into local optimal solution; second, on the basis of AGRC, a parallel model is designed for fuzzy granulation of data to construct granular space, which can greatly enhance the efficiency compared with serial granulation of data; third, in the fuzzy granular space, we design RFGDT to classify the fuzzy granules, which can select important features as tree nodes based on information gain ratio and avoid the problem of overfitting based on the pruning algorithm proposed. Finally, we employ the dataset from UC Irvine Machine Learning Repository for verification. Theory and experimental results prove that RFGDT has high efficiency and accuracy and is robust in solving classification problems.

show abstract

“…Source [10] developed a new decision tree algorithm for processing large datasets in a fast and memory efficient way. Several decision tree algorithms were developed before, but these had two issues: the entire training dataset had to be loaded in memory and parameterizing them was a complicated task.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several decision tree algorithms were developed before, but these had two issues: the entire training dataset had to be loaded in memory and parameterizing them was a complicated task. The improvement of source's [10] decision tree over the previous ones was that it did not store every training observation in memory, instead, loaded them one by one, and updated the leaves accordingly. If more than a set number of observations were assigned to a leaf, then a cut and reassignment was performed.…”

Section: Literature Reviewmentioning

confidence: 99%

Processing Intrusion Data with Machine Learning and MapReduce

Brunner¹

2017

AARMS

View full text Add to dashboard Cite

These past years, cyber-attacks became a daily issue for enterprises. A possible defence against this kind of threat is intrusion detection. One of the key challenges is information extraction from this large amount of logged data. My paper aims to identify cyber-attack types as patterns in log files using advanced parallel computing approach and machine learning techniques. The MapReduce programming model is applied to parallel computing, while decision tree algorithms are used from machine learning.I discuss two research questions in this paper. First, despite parallelization, are machine learning algorithms still able to provide results with acceptable accuracy measured by traditional data mining figures (accuracy, precision, recall, area under receiver operand characteristic [ROC] curve [AUC])? Second, is it possible to achieve significant performance improvement by measuring runtime execution of the algorithm by introducing several measurement points?I proved that the machine learning model with two categories in the target variable is preferred to the one having five categories. The average performance improvement was 4–5 times faster for the whole algorithm compared to a single core solution. I achieved most of these improvements during the data transfer phase.

show abstract

Building fast decision trees from large training sets

Cited by 18 publications

References 27 publications

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Random Fuzzy Granular Decision Tree

Processing Intrusion Data with Machine Learning and MapReduce

Contact Info

Product

Resources

About