Adaptive three-phase support vector data description

Rahmanimanesh, Mohammad; Nasiri, Jalal A.; Jalili, Saeed; Charkari, Nasrollah Moghadam

doi:10.1007/s10044-017-0646-3

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…For classification tasks, different machine learning classifiers have been applied. The recent research indicates that Support Vector Machines has better performance among other classifiers in most cases [6][7][8][9][10][11]. However, SVM-based classifiers suffer from several major problems.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

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Support Vector Machine classifiers are widely used in many classification tasks. However, they have two considerable weaknesses, Unclassifiable Region (UR) in multiclass classification and outliers. In this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear and polynomial kernel functions. PLS-TSVM not only solves the unclassifiable region problem by introducing a continuous output value membership function, but it also reduces the adverse effects of noisy data and outliers. For showing the superiority of our proposed method, we have conducted experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of classification accuracy. Moreover, PLS-TSVM has been evaluated against "one-against-all" and "oneagainst-one" approaches on several well-known video datasets such as Weizmann, KTH, and UCF101 for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and 12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM classifiers. The final results indicate that the proposed algorithm could achieve better overall performances than the literature.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

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“…In [16], a terrain classification method for ensuring navigation safety of mobile service robots based on SVDD is proposed. To enhance the performance of SVDD, numerous extensions and hybridization techniques have been proposed [8], [17]- [21]. The main extensions of SVDD can be categorized into four main categories.…”

Section: Introductionmentioning

confidence: 99%

Ellipsoidal Subspace Support Vector Data Description

et al. 2020

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In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to the data description in the subspace by hyperspherical encapsulation of target class data. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve competing results and show clear improvement compared to the other support vector based methods. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.

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“…Sequence-Based Prediction of Proteinâ€"Peptide(SPRINT) method was used to the prediction of Proteinâ€"peptide Residue-level Interactions by SVM 14 . SVM implements the structural risk minimization (SRM) that minimized the upper bound of generation error 30,31 . Jones et al 32 suggested the DeepCov method which employed convolutional neural networks to operate on amino acid pair frequency and covariance data that extract from sequence alignments.…”

Section: Introductionmentioning

confidence: 99%

A novel fusion based on the evolutionary features for protein fold recognition using support vector machines

Refahi

Mir

Nasiri

2020

Sci Rep

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protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionarybased information and physicochemical-based information to extract features. In recent years, finding an efficient technique for integrating discriminate features have been received advancing attention. in this study, we integrate Auto-cross-covariance and Separated dimer evolutionary feature extraction methods. The results' features are scored by Information gain to define and select several discriminated features. According to three benchmark datasets, DD, RDD ,and eDD, the results of the support vector machine show more than 6 % improvement in accuracy on these benchmark datasets. Proteins are Jack of all trades biological macromolecules. They are involved in almost every biological reaction; Protein plays a critical role in many different areas such as building muscle, hormone production, enzyme, immune function, and energy. Typically more than 20,000 proteins exist in human cells 1 , to acquire knowledge about the protein function and interactions, the prediction of protein structural classes is extremely useful 2. Fold recognition is one of the fundamental methods in protein structure and function prediction. Each type of protein has a particular three-dimensional structure, which is determined by the order of the amino acids in its polypeptide chain. A protein' s structure begins with its amino acid sequence, which is thus considered its primary structure. The next level of the organization includes the α helix and β sheets that forms with certain segments of the polypeptide chain; these folds are elements of secondary structure. The full, threedimensional conformation formed by an entire polypeptide chain is referred to as tertiary structure 3. One of the main steps which can be assumed as a vital stage for predicting protein fold(secondary structure) is feature extraction. Computational feature extraction methods are divided into syntactical, physicochemical and evolutionary methods. Syntactical methods pay attention only to the protein sequence, like composition and occurrence 4-6. physicochemical methods consider some physical and chemical properties of protein sequences. Evolutionary methods extract features from Basic Local Alignment Search Tool(BLAST). When attempting to solve many biological problems, it is obvious that a single data source might not be informative, and combining several complementary biological data sources will lead to a more accurate result. When we studied methods of protein fold recognition, we found that less attention has been paid to the fusion of features to get more comprehensive features. In recent studies, researchers attempted to find new feature extraction methods 7-12 or t...

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Adaptive three-phase support vector data description

Cited by 5 publications

References 31 publications

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Ellipsoidal Subspace Support Vector Data Description

A novel fusion based on the evolutionary features for protein fold recognition using support vector machines

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