Handling binary classification problems with a priority class by using Support Vector Machines

Abril, Luis González; Ángulo, Cecilio; Núñez, Haydemar; Leal, Yenny

doi:10.1016/j.asoc.2017.08.023

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…For regression problems, the functions of the kernel are often to be used to predict resulting outcome, such as radial basis function (RBF), two neural networks, polynomial, sigmoid and linear, exponential radial basis function (ERBF) [55,56]. In recent years, SVM has been applied in many fields as well as publications, therefore, the details of the SVM are not presented in this study but can be found in [57][58][59][60][61][62][63].…”

Section: Svmmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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Section: Svmmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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“…Since, in the dataset, the number of instances in every class remains imbalanced (see Table 1), the use of accuracy or precision as the main performance metric can imply a significant skew (Chawla, 2005). It is therefore preferred to use sensitivity and specificity since these remain unbiased metrics even when the classes are imbalanced (Gonzalez-Abril et al, 2014;Gonzalez-Abril et al, 2017). Therefore, when a single metric is required for the comparison of classifier results (i.e.…”

Section: Classification Performance Metricsmentioning

confidence: 99%

Peer Review #2 of "Temporally-aware algorithms for the classification of anuran sounds (v0.1)"

2018

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Several authors have shown that the sounds of anurans can be used as an indicator of climate change. Hence, the recording, storage and further processing of a huge number of anuran sounds, distributed over time and space, are required in order to obtain this indicator. Furthermore, it is desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper, six classification methods are proposed, all based on the data-mining domain, which strive to take advantage of the temporal character of the sounds. The definition and comparison of these classification methods is undertaken using several approaches. The main conclusions of this paper are that: i) the sliding window method attained the best results in the experiments presented, and even outperformed the Hidden Markov Models usually employed in similar applications; ii) noteworthy overall classification performance has been obtained, which is an especially striking result considering that the sounds analysed were affected by a highly noisy background; iii) the instance selection for the determination of the sounds in the training dataset offers better results than cross-validation techniques; and iv) the temporally-aware classifiers have revealed that they can obtain better performance than their non-temporally-aware counterparts.

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“…Back-propagation neural network and SVM algorithms have been widely used for classification, identification, prediction, and detection that produce a fairly good degree of accuracy. The applications of back-propagation neural network and SVM for classification are the fruit classification, ship classification, natural gas pipeline classification, automatic text classification, cancer classification, audio sounds classification, handling binary classification, enzyme classification and object classification [6][7][8][9][10][11][12][13][14][15]. The applications of the two classifiers for identification are defect identification for simple fleshy fruits, hand writer character recognition, transcription factor binding sites identification on human genome, diagnosis of renal calculus disease, and automated speech signal analysis [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Motorcycle on the Road using Digital Image Processing

Sutikno

Wibawa²,

Saputra³

2018

SJI

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Traffic accident is one of the causes of death in the world. One of them is traffic accidents on motorcyclist not wearing helmets. To overcome this problem, several researchers have developed detection system of motorcyclist not wear helmet. This system consists of motorcycle detection and motorcyclist head detection. On motorcycle detection, accuracy still needs to be improved. For this reason, this paper proposed motorcycle detection by adding image improvement processes that are enhancing contrast and adding object positioning features.The techniques proposed in this study are divided into 3 stages of image enhancement, feature extraction, and classification. The image enhancement stage consists of enhance contrast, convert RGB image to gray scale, background subtraction, convert gray scale image to binary, closing operation, and small object removal. The features used in this paper are the features of the object area, the circumference of the object, and the location of the object, while the method for classification process using back-propagation neural network and SVM. The proposed method resulted in an accuracy of 96.97%. Error occurs in all image test data not motorcycle objects detected as motorcycle objects. This error is caused because the pixel value between the objects in the image with the background color has a level of difference is too small, so it is detected as an object not a motorcycle.

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Handling binary classification problems with a priority class by using Support Vector Machines

Cited by 18 publications

References 23 publications

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Peer Review #2 of "Temporally-aware algorithms for the classification of anuran sounds (v0.1)"

Automatic Detection of Motorcycle on the Road using Digital Image Processing

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