An Ensemble Random Forest Algorithm for Insurance Big Data Analysis

Lin, Weiwei; Wu, Ziming; Lin, Lei; Wen, Angzhan; Li, Jin

doi:10.1109/access.2017.2738069

Cited by 264 publications

(101 citation statements)

References 16 publications

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“…, x l (172)] and y l ∈ {1, 2, 3, 4, 5, 6, 7, 8} denote the input 172 features and the output label of sample l, the general idea of random forest can be described as, (1) Randomly select N samples with replacement from the original dataset, and obtain N subsamples for constructing each tree. As an ensemble model, random forest model fits the input data in a shorter time as each decision tree is independent, making parallel computing and modeling possible [31,32]. We also test the performance of the other five machine learning methods, including Fine Tree, Radial Basis Function kernel Support Vector Machine (RBF SVM), Weighted K-Nearest Neighbors (KNN), Linear Discriminant and Subspace KNN [33,34].…”

Section: Random Forest For Image Classificationmentioning

confidence: 99%

“…As an ensemble model, random forest model fits the input data in a shorter time as each decision tree is independent, making parallel computing and modeling possible [31,32]. We also test the performance of the other five machine learning methods, including Fine Tree, Radial Basis Function kernel Support Vector Machine (RBF SVM), Weighted K-Nearest Neighbors (KNN), Linear Discriminant and Subspace KNN [33,34].…”

Section: Random Forest For Image Classificationmentioning

confidence: 99%

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Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

et al. 2019

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Featured Application: Maceral Analysis; Coal Processing.Abstract: An intelligent analytical technique which is able to accurately identify maceral components is highly desired in the fields of mining and geology. However, currently available methods based on fixed-size window neglect the shape information, and thus do not work in identifying maceral composition from one entire photomicrograph. To address these concerns, we propose a novel Maceral Identification strategy based on image Segmentation and Classification (MISC). Considering the complex and heterogeneous nature of coal, a two-level coarse-to-fine clustering method based on K-means is employed to divide microscopic images into a sequence of regions with similar attributes (i.e., binder, vitrinite, liptinite and inertinite). Furthermore, comprehensive features along with random forest are utilized to automatically classify binder and seven types of maceral components, including vitrinite, fusinite, semifusinite, cutinite, sporinite, inertodetrinite and micrinite. Evaluations on 39 microscopic images show that the proposed method achieves the state-of-the-art accuracy of 90.44% and serves as the baseline for future research on maceral analysis. In addition, to support the decisions of petrologists during maceral analysis, we developed a standalone software, which is freely available at https:/github.com/GuyooGu/MISC-Master.

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Section: Random Forest For Image Classificationmentioning

confidence: 99%

Section: Random Forest For Image Classificationmentioning

confidence: 99%

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

et al. 2019

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“…An Ensemble Random Forest Algorithm for Insurance Big Data Analysis [4] by W. Lin proposed an A group arbitrary woods calculation which utilized the parallel registering capacity and memory-store system improved by Spark. The proposed technique The group arbitrary backwoods calculation outflanked SVM and other characterization calculations in both execution and exactness utilizing the imbalanced information, and it is helpful for improving the precision of item showcasing contrast with the customary fake methodology.…”

Section: A Enormous Data Classificationmentioning

confidence: 99%

“…Enormous information innovation was generally connected. In the scholastic network, the regarded diaries "Nature" and "Science" have separately propelled huge information issues named "Enormous Data" and "Manage Data", which examine an assortment of issues experienced in huge information innovation from the Internet innovation, financial matters, supercomputing, organic sciences, drug and numerous different viewpoints [4]. The monstrous volume of data assembled by contemporary frameworks wound up inescapable, the same number of research exercises require gathering progressively immense measures of information [1].…”

Section: Introductionmentioning

confidence: 99%

The Deep Learning and Apache Spark Enabled Architecture for Improving the Performance of Big Data Classification

Brahmane¹,

Krishna²

2019

IJITEE

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At present the Big Data applications, for example, informal communication, therapeutic human services, horticulture, banking, financial exchange, instruction, Facebook and so forth are producing the information with extremely rapid. Volume and Velocity of the Big information assumes a significant job in the presentation of Big information applications. Execution of the Big information application can be influenced by different parameters. Expediently search, proficiency and precision are the a portion of the overwhelming parameters which influence the general execution of any Big information applications. Due the immediate and aberrant inclusion of the qualities of 7Vs of Big information, each Big Data administrations anticipate the elite. Elite is the greatest test in the present evolving situation. In this paper we propose the Big Data characterization way to deal with speedup the Big Data applications. This paper is the review paper, we allude different Big information advancements and the related work in the field of Big Data Classification. In the wake of learning and understanding the writing we discover the holes in existing work and techniques. Finally we propose the novel methodology of Big Data characterization. Our methodology relies on the Deep Learning and Apache Spark engineering. In the proposed work two stages are appeared; first stage is include choice and second stage is Big Data Classification. Apache Spark is the most reasonable and predominant innovation to execute this proposed work. Apache Spark is having two hubs; introductory hubs and last hubs. The element choice will be occur in introductory hubs and Big Data Classification will happen in definite hubs of Apache Spark.

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“…Second, random forest is suitable for handling large data due to its parallelization [28]. It has been combined with the Spark [28], heuristic bootstrap sampling method [29], kernel principal component analysis [30], and other technologies to perform fault diagnosis and regression tasks [31,32]. Owing to the improvement of the forecasting accuracy for highdimensional and large-scale wind turbine data, we propose an optimized random forest method which consists of a dimension reduction procedure and the weighted voting process for the short-term WPF.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

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An Ensemble Random Forest Algorithm for Insurance Big Data Analysis

Cited by 264 publications

References 16 publications

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

Intelligent Identification of Maceral Components of Coal Based on Image Segmentation and Classification

The Deep Learning and Apache Spark Enabled Architecture for Improving the Performance of Big Data Classification

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

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