An idea of a clustering algorithm using support vector machines based on binary decision tree

Elaidi, Halima; Elhaddar, Younes; Benabbou, Zahra; Abbar, Hassan

doi:10.1109/isacv.2018.8354024

Cited by 31 publications

(17 citation statements)

References 4 publications

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“…The purpose is to find the optimal classification hyperplane, where the maximum margin between two classes is obtained. SVM obtains the kernel function parameters from the training datasets and uses them to classify the testing datasets [ 62 ].…”

Section: Classifiermentioning

confidence: 99%

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Jiang

Zhou

Ling

et al. 2021

Sensors

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Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.

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Section: Classifiermentioning

confidence: 99%

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Jiang

Zhou

Ling

et al. 2021

Sensors

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“…It is often used in various classi cation and regression problems, or used in outlier detection, and also used in unsupervised learning classi cation problems. [12] 2.2.6 GBDT Gradient Boosting Decision Tree is the abbreviation of Gradient Boosting Decision Tree. The general concept of Boosting is to gradually optimize through a series of learning, and to increase the weight of the parts that have not been well divided in the past to strengthen learning; the concept of Gradient Boosting is in a series of learning Establish a new model for the gradient descent direction of the loss function of the previous model, amplify the weight of the wrong part in disguise, and strive to make the loss function smaller, the better the overall performance.…”

Section: Random Forestmentioning

confidence: 99%

“…Faced with projects that are never nished, every speci cation change is tormenting. In order to be simple and fast, the program structure of the stacked bed frame also makes the enterprise virtually impossible with less technical debt, you don't know when potential problems will emerge due to the lack of quality assurance [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Using AI technology to customize manufacture product label for decision making

Zhang

Dong

2021

Preprint

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When the manufacturing industry is dealing with information technology, it has to face a large number of parameters and frequent adjustments. How to correctly import and maintain it has always been a huge challenge. Once the setting is wrong, it will bring losses, ranging from poor products that require maintenance, heavy work or scrapping, and at worst, resulting in production line shutdown, reduced factory productivity, delayed shipments and other adverse consequences. In order to improve this problem, this study uses the data in the approval form of a customized label set by an electronic manufacturer and use artificial intelligence models to find out the hidden rules behind a large number of customized labels, through data processing and model building. Model and parameter experiments are used to improve the effectiveness of artificial intelligence models, and for the problem of time characteristics but uneven distribution of data, the method of cyclic testing is adopted to increase the diversity of the test set. The results of this paper, we integrate each stage and an auxiliary decision-making is established. When the user's setting is inconsistent with the predicted result, a warning will be displayed to speed up the operation process, reduce the scope of confirmation, and ultimately reduce the error rate, thereby improving the problem, reducing scrap and production line shutdown to improve factory productivity. In the statics, the accuracy rate of new recruits was only 80%. The accuracy rate of the artificial intelligence model can be increased to 95%. The number of stoppages is reduced from 4 times per month to 1 time per month. Under full capacity, this assistance the decision-making system can reduce loss cost.

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“…(3) Support Vector Machine (SVM) is SVM is a wellknown classification technique based on theory statistical learning [14]. SVM consists of a two classes classification model; a model defined in the feature space of the largest linear classifier [15];…”

Section: Abstract-big Data Classification Cooperative Historical Loan...mentioning

confidence: 99%

Comparison of Classification Methods using Historical Loan Application Data

Kelen

Emanuel

2019

2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

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An idea of a clustering algorithm using support vector machines based on binary decision tree

Cited by 31 publications

References 4 publications

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey

Using AI technology to customize manufacture product label for decision making

Comparison of Classification Methods using Historical Loan Application Data

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