Comparison of the accuracy of SVM kemel functions in text classification

Kalcheva, Neli; Karova, Milena; Penev, Ivaylo

doi:10.1109/bia50171.2020.9244278

Cited by 34 publications

(11 citation statements)

References 4 publications

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“…Figure 6 shows the classification performance comparisons of four algorithms when increasing attributes. Furthermore, the results from each function in this approach give a similar trend result of Bui et al [2012], Ravi [2016] and Kalcheva et al [2020], which is reported that the linear function had better performance than the radial basis function, sigmoid function, and polynomial function. Due to linear function is the best function to deal with the linear data type and binary and multiply class [Fan et al, 2008], while radial basis function, polynomial function and sigmoid function are powerful ability to classify the nonlinear data and s curve data type [Keskes and Braham, 2014].…”

Section: Selected Algorithm For Classification Approach Developmentsupporting

confidence: 82%

Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

Sillberg¹,

Kullavanijaya²,

Chavalparit³

2021

J. Ecol. Eng.

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The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River's water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH 3 -N), nitrate (NO 3 -N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH 3 -N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water's quality with 0.86-0.95 accuracy when applied three to six attributes.

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Section: Selected Algorithm For Classification Approach Developmentsupporting

confidence: 82%

Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

Sillberg¹,

Kullavanijaya²,

Chavalparit³

2021

J. Ecol. Eng.

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“…Where 𝛼 is the scaling parameter of the sample while 𝑟 is the shifting parameter for threshold mapping of the transpose 𝑇 of the two points 𝑥 and 𝑦. More information on the kernels can be found in [17]- [19].…”

Section: 𝑘(𝑥 𝑦) = 𝑡𝑎𝑛ℎ (𝛼 𝑥𝑇 𝑦 + 𝑟 )mentioning

confidence: 99%

A comparison of different support vector machine kernels for artificial speech detection

Tan

Hijazi²,

Nohuddin³

2023

TELKOMNIKA

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As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.

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“…Table VIII describes the prediction performance of the proposed crime type prediction models. In addition, for performance comparison, SVM with polynomial kernel function of degree 2, and Bernoulli Naïve Bayes algorithms are also considered [29]- [30]. The table describes the computed values of performance evaluation parameters including accuracy, precision, recall and F1-Score.…”

Section: A Crime Type Predictionmentioning

confidence: 99%

Smart Policing Technique With Crime Type and Risk Score Prediction Based on Machine Learning for Early Awareness of Risk Situation

Baek

Park

Park³

et al. 2021

IEEE Access

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In order to quickly and effectively respond to a newly received criminal case, information regarding the type and severity of the case is crucial for authorities. This paper designs and develops a crime type and risk level prediction technique based on machine learning technology and verifies its performance. The designed technology can predict crime type and crime risk level using a text-based criminal case summary, which is criminal case receipt data. For the text-based criminal case summary data, the KICS data format is considered, which is actual policing data that contains information about criminal cases. For the crime type, 21 representative types of crimes are considered; therefore, the system can predict one of 21 types of crime for each criminal case. Furthermore, to predict the crime risk level, we developed a crime risk calculation formula. The developed formula calculates the crime risk level and outputs the risk score in numerical terms considering the severity and damage level of the criminal case. To predict the crime type and crime risk score, both DNN and CNN-based prediction models were designed and developed. The performance evaluation section shows that, in the case of crime type prediction, the proposed prediction models can achieve better performance than traditional classification algorithms such as naïve Bayes and SVM. The performance of the CNN-based crime type prediction model is about 7% and 8% better than those of the SVM algorithm and the naïve Bayes algorithm, respectively. The performance of the designed technology was comprehensively analyzed and verified through various performance measurement parameters. It is also developed in the form of a software platform with a GUI, allowing field personnel (e.g. police officers) to intuitively identify the type of criminal case and the level of risk from a text-based criminal case summary upon receipt of a new criminal case.

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Comparison of the accuracy of SVM kemel functions in text classification

Cited by 34 publications

References 4 publications

Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River

A comparison of different support vector machine kernels for artificial speech detection

Smart Policing Technique With Crime Type and Risk Score Prediction Based on Machine Learning for Early Awareness of Risk Situation

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