Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification

Weinberg, Abraham Itzhak; Last, Mark

doi:10.1186/s40537-019-0186-3

Cited by 37 publications

(21 citation statements)

References 34 publications

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“…The first ensemble method was used by combining two linear regression models and nearest neighbor method to enhance the performance of recognition systems [344]. To determine the target variable, decision trees are very often used as ensemble method [345,346].…”

Section: Ensemble Based Methodsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Ensemble Based Methodsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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show abstract

“…Both RF and XGBoost are based on the classification and regression trees (CART). CART has a well-known problem related to the model instability in which the tree structure changes significantly by small change in training data [67]. RF and XGBoost adopt different approaches to mitigating the weakness of CART.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea

Park

Lee

Yoo

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Near-surface relative humidity (RHns) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RHns estimation is difficult due to the spatial discontinuity of in-situ observations and the cloud contamination of satellite-based data. This study proposed machine learning-based models to estimate spatially continuous daily RHns at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RHns. All sky estimation of RHns using machine learning has been rarely conducted, and it can be an alternative to the currently available RHns data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RHns conditions (i.e., < 30% or > 70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest and XGBoost were trained and validated using 94 in-situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than random forest, and the spatially continuous RHns model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root mean square error of 10.61%. Spatiotemporal patterns of the estimated RHns agreed with in-situ observations, reflecting the effect of topography on RHns. We expect that the proposed RHns model could be used in various environmental studies that require RHns under all sky conditions as input data.

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“…Text to speech conversion in the case of CRM automation, hard real-time applications, aircraft systems, fraud detection during online transactions, dynamic systems, and other applications require fast classification to take timely necessary remedial actions. In big data scenarios, in the Map phase, if it is required to predict the class label for a huge number of instances, an efficient method is much useful to save the processing time [16].…”

Section: Introductionmentioning

confidence: 99%

“…Ahmad Ashari et al have studied the classification speed of three standard classifiers [17] and opined that the decision tree's performance is relatively high. Weinberg et al [16] have proposed a method to improve classification speed when the ensemble of decision trees is constructed on big data. Their method finds the best representative tree from the ensemble on which classification is performed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

Muneiah

Rao

2020

ELCVIA

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So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree's classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method.

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Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification

Cited by 37 publications

References 34 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea

A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

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