Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Arana‐Daniel, Nancy; Álvarez-Gallegos, Alberto; López-Franco, Carlos; Alanís, Alma Y.; Morales, J. Alejandro; Lopez-Franco, Adriana

doi:10.4137/ebo.s40912

Cited by 10 publications

(15 citation statements)

References 77 publications

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“…Dalam melakukan penelitian ini, kami bertujuan untuk mendapatkan prediksi hasil pembelajaran berbasis e-learning yang lebih baik dari sebelumnya, yaitu menggunakan metode SVM. Algoritma SVM IJAIDM [3]. Inti dari menggunakan metode SVM merupakan salah satu dari yang paling banyak algoritma learning yang efisien, yang banyak digunakan untuk pengenalan pola sejak diperkenalkan pada tahun 1990-an.…”

Section: Pendahuluanunclassified

Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

Saputra

Putri²,

Indriyanti³

2019

IJAIDM

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Prediction is a systematic estimate that identifies past and future information, we predict student learning success with e-learning based on a log of student activities. In this study, we use the Support vector Machine (SVM) method, which is compared with Particle Swarm Optimization. The problem with this algorithm is that the SVM has a very good generalization that can solve a problem. However, some of the attributes in the data can reduce accuracy and add complexity to the SVM algorithm. For this reason, attribute selection for existing data is needed, therefore Particle Swarm Optimization (PSO) method is applied for the right attribute selection in determining the success of elearning learning based on student activity logs, because the PSO method can improve accuracy in determining selection of attributes. The SVM algorithm produces an accuracy value of 88.00% and AUC with a value of 0.8120, while with SVM Based on PSO the accuracy value is 88.50% and the AUC value is 0.8460. Therefore, there is an increase from the result of an accuracy value of 0.50% and an AUC value of 3.40%, and then the result is in good classification.

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Section: Pendahuluanunclassified

Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

Saputra

Putri²,

Indriyanti³

2019

IJAIDM

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“…If αi > 0, x1 is called a support vector b is a regulation parameter used to trade-off the training accuracy and the model complexity so that a superior generalization capability can be achieved. K is a kernel function, which is used to measure the similarity between two or much more samples [13].…”

Section: Data Of Another Classmentioning

confidence: 99%

Destek Vektör Makineleri Kullanılarak Spam SMS Tespiti

Tekerek

2019

Politeknik Dergisi

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Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using Data Mining (DM) methods. In the proposed study, data mining algorithms such as Naive Bayes (NB), K-Nearest Neighborhood (KNN), Support Vector Machine (SVM), Random Forest (RF) and Random Tree (RT) is selected. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed study achieved 98.33 % success rate and 0,087 false positive rate for SVM algorithm..

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“…Genetic algorithms were introduced in the 1970s as a class of evolutionary algorithms [22,24]. These are heuristic approaches that find solutions based on evolutionary biology concepts.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…These are heuristic approaches that find solutions based on evolutionary biology concepts. Genetic algorithms select a subset of features by removing unimportant features [22]. In this study the GA algorithm is also used to select best SVM kernel function.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Facial Expression Detection for video sequences using local feature extraction algorithms

Chengeta¹,

Viriri²

2019

SIPIJ

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Facial expression image analysis can either be in the form of static image analysis or dynamic temporal 3D image or video analysis. The former involves static images taken on an individual at a specific point in time and is in 2-dimensional format. The latter involves dynamic textures extraction of video sequences extended in a temporal domain. Dynamic texture analysis involves short term facial expression movements in 3D in a temporal or spatial domain. Two feature extraction algorithms are used in 3D facial expression analysis namely holistic and local algorithms. Holistic algorithms analyze the whole face whilst the local algorithms analyze a facial image in small components namely nose, mouth, cheek and forehead. The paper uses a popular local feature extraction algorithm called LBP-TOP, dynamic image features based on video sequences in a temporal domain. Volume Local Binary Patterns combine texture, motion and appearance. VLBP and LBP-TOP outperformed other approaches by including local facial feature extraction algorithms which are resistant to gray-scale modifications and computation. It is also crucial to note that these emotions being natural reactions, recognition of feature selection and edge detection from the video sequences can increase accuracy and reduce the error rate. This can be achieved by removing unimportant information from the facial images. The results showed better percentage recognition rate by using local facial extraction algorithms like local binary patterns and local directional patterns than holistic algorithms like GLCM and Linear Discriminant Analysis. The study proposes local binary pattern variant LBP-TOP, local directional patterns and support vector machines aided by genetic algorithms for feature selection. The study was based on Facial Expressions and Emotions (FEED) and CK+ image sources.

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Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Cited by 10 publications

References 77 publications

Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

Prediction of Successful Elearning Based on Activity Logs with Selection of Support Vector Machine based on Particle Swarm Optimization

Destek Vektör Makineleri Kullanılarak Spam SMS Tespiti

Facial Expression Detection for video sequences using local feature extraction algorithms

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