The comparison of optimizing SVM by GA and grid search

Sun, Yifei; Wang, Yongming; Guo, Lili; Ma, Zhong‐Qi; Shan, Jin

doi:10.1109/icemi.2017.8265815

Cited by 33 publications

(14 citation statements)

References 12 publications

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“…To achieve significant clinical application, the selected feature set based on FSFS was aimed to achieve target criteria in AUROC in three AHI cutoffs during the selection procedure. Eventually, the selected feature set after the two-stage feature selection was used to establish the prediction model for OSA recognition based on SVM [ 24 ]. The posterior probability of the SVM was used to determine the class of the incoming datum [ 25 ], either OSA or non-OSA.…”

Section: Methodsmentioning

confidence: 99%

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

Huang

Lee

Liu

et al. 2020

Sleep

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Study Objectives Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice. Methods The SVM model was developed using the features routinely collected at the clinical evaluation from 6,875 Chinese patients referred to sleep clinics for suspected OSA. Three apnea-hypopnea index (AHI) cutoffs, ≥5/h, ≥15/h, and ≥30/h were used to define the severity of OSA. The continuous and categorized features were selected separately and were further selected through stepwise forward feature selection. The modeling was achieved through fivefold cross-validation. The model discriminative ability was evaluated for the whole data set and four subgroups categorized with gender and age (<65 versus ≥65 years old [y/o]). Results Two features were selected to predict AHI cutoff ≥5/h with six features selected for ≥15/h, and six features selected for ≥30/h, respectively, to reach Area under the Receiver Operating Characteristic (AUROC) 0.82, 0.80, and 0.78, respectively. The sensitivity was 74.14%, 75.18%, and 70.26%, while the specificity was 74.71%, 68.73%, and 70.30%, respectively. Compared to logistic regression, Berlin questionnaire, NoSAS Score, and Supersparse Linear Integer Model (SLIM) scoring system, the SVM model performs better with a more balanced sensitivity and specificity. The discriminative ability was best for male <65 y/o and modest for female ≥65 y/o. Conclusion Our model provides a simple and accurate modality for early identification of patients with OSA and may potentially help prioritize them for sleep study.

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Section: Methodsmentioning

confidence: 99%

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

Huang

Lee

Liu

et al. 2020

Sleep

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“…The remainder of the botnet instances served as the testing set. For optimization, the Grid Search algorithm [80] was used. With regard to CICIDS2018, the RF and DT learners scored an accuracy of 99.99%.…”

Section: Huancayo Ramos Et Al [25] (Benchmark-based Reference Model mentioning

confidence: 99%

“…CIC-IDS2017, which contains five days of network traffic, was released to remedy the deficiencies of its predecessor. Among the many benefits of this new dataset, the high number of features (80) facilitates machine learning. CICIDS2017 contains 2,830,743 instances, with attack traffic amounting to about 19.7 % of this total number.…”

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confidence: 99%

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

2020

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The exponential growth in computer networks and network applications worldwide has been matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 were created to train predictive models on network-based intrusion detection. These datasets are not meant to serve as repositories for signature-based detection systems, but rather to promote research on anomaly-based detection through various machine learning approaches. CSE-CIC-IDS2018 contains about 16,000,000 instances collected over the course of ten days. It is the most recent intrusion detection dataset that is big data, publicly available, and covers a wide range of attack types. This multi-class dataset has a class imbalance, with roughly 17% of the instances comprising attack (anomalous) traffic. Our survey work contributes several key findings. We determined that the best performance scores for each study, where available, were unexpectedly high overall, which may be due to overfitting. We also found that most of the works did not address class imbalance, the effects of which can bias results in a big data study. Lastly, we discovered that information on the data cleaning of CSE-CIC-IDS2018 was inadequate across the board, a finding that may indicate problems with reproducibility of experiments. In our survey, major research gaps have also been identified.

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“…Therefore, the hyperparameters of the model are important for practical implementation. Some procedures are discussed to decide the best hyperparameter, such as random search (RS) [ 28 ] and grid search (GS) [ 29 ]. The former may generate good hyperparameters faster, but optimization is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

“…The training or test data will affect the training result. To avoid this situation, cross validation is used [ 22 , 29 , 30 ]. Moreover, an early stopping scheme is also adopted when searching for the best hyperparameters, which prevent overfitting [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification

Hung

Zeng

Lee

et al. 2021

Sensors

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This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.

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The comparison of optimizing SVM by GA and grid search

Cited by 33 publications

References 12 publications

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification

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