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Reducing the Number of support vectors of SVM classifiers Using the Smoothed Separable Case Approximation

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2012年第4期23卷 682-688页

作者： Geebelen, Dries Suykens, Johan A. K. Vandewalle, Joos Katholieke Univ Leuven Dept Elect Engn ESAT SCD SISTA B-3001 Heverlee Belgium

In this brief, we propose a new method to reduce the number of support vectors of support vector machine (SVM) classifiers. We formulate the approximation of an SVM solution as a classification problem that is separable in the feature space. Due to the separability, the hard-margin SVM can be used to solve it. This approach, which we call the separable case approximation (SCA), is very similar to the cross-training algorithm explained in [1], which is inspired by editing algorithms [2]. The norm of the weight vector achieved by SCA can, however, become arbitrarily large. For that reason, we propose an algorithm, called the smoothed SCA (SSCA), that additionally upper-bounds the weight vector of the pruned solution and, for the commonly used kernels, reduces the number of support vectors even more. The lower the chosen upper bound, the larger this extra reduction becomes. Upper-bounding the weight vector is important because it ensures numerical stability, reduces the time to find the pruned solution, and avoids overfitting during the approximation phase. On the examined datasets, SSCA drastically reduces the number of support vectors.

关键词： Run time complexity sparse approximation support vector machine classifier

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Combining classifiers for improved classification of proteins from sequence or structure

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BMC BIOINFORMATICS 2008年第1期9卷 389-389页

作者： Melvin, Iain Weston, Jason Leslie, Christina S. Noble, William S. Univ Washington Dept Comp Sci & Engn Dept Genome Sci Seattle WA 98195 USA NEC Labs Amer Princeton NJ USA Mem Sloan Kettering Canc Ctr Sloan Kettering Inst Computat Biol Program New York NY 10021 USA

Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage. Results: In this study, we develop a hybrid machine learning approach for classifying proteins, and we apply the method to the problem of assigning proteins to structural categories based on their sequences or their 3D structures. The method combines a full-coverage but lower accuracy nearest neighbor method with higher accuracy but reduced coverage multiclass SVMs to produce a full coverage classifier with overall improved accuracy. The hybrid approach is based on the simple idea of "punting" from one method to another using a learned threshold. Conclusion: In cross-validated experiments on the SCOP hierarchy, the hybrid methods consistently outperform the individual component methods at all levels of coverage.

关键词： support vector machine support vector machine classifier Near Neighbor Hybrid classifier Multiclass support vector machine

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Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

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BMC BIOINFORMATICS 2012年第1期13卷 1-20页

作者： Walia, Rasna R. Caragea, Cornelia Lewis, Benjamin A. Towfic, Fadi Terribilini, Michael El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Iowa State Univ Bioinformat & Computat Biol Program Ames IA 50011 USA Iowa State Univ Dept Comp Sci Ames IA USA Iowa State Univ Ctr Computat Intelligence Learning & Discovery Ames IA USA Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Iowa State Univ Dept Genet Dev & Cell Biol Ames IA USA Broad Inst Cambridge MA USA Elon Univ Dept Biol Elon NC USA Al Azhar Univ Dept Syst & Comp Engn Cairo Egypt

Background: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. Results: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naive Bayes (NB) and support vector machine (SVM)) using three different data representations in which features are encoded using either sequence-or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix

关键词： support vector machine classifier Radial Basis Function Kernel Matthews Correlation Coefficient Interface Residue Target Residue

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Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images

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REMOTE SENSING 2021年第10期13卷 1960-1960页

作者： Sharma, Amit Hubert-Moy, Laurance Buvaneshwari, Sriramulu Sekhar, Muddu Ruiz, Laurent Moger, Hemanth Bandyopadhyay, Soumya Corgne, Samuel Univ Rennes Ctr Natl Rech Sci Unite Mixte Rech Littoral EnvironmGeomatTeledetect F-35043 Rennes France Indian Inst Sci Indo French Cell Water Sci Bangalore 560012 Karnataka India Indian Inst Sci Dept Civil Engn Bangalore 560012 Karnataka India Univ Toulouse III Paul Sabatier Ctr Natl Rech Sci Inst Rech Dev Unite Mixte Rech LittoralGeosci Environm F-31401 Toulouse France Inst Natl Rech Agr Alimentat & Environm Unite Mixte Rech Littoral Sol Agro & Hydrosyst Sp AgroCampus Ouest F-35043 Rennes France Indian Space Res Org Earth Observat & Disaster Management Programme Of Bangalore 560094 Karnataka India

Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study's main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014-2016 using ground observations. Kappa coefficients ranged from 0.81-0.96 for 2014-2015 and 0.62-0.89 for 2015-2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions.

关键词： groundwater irrigation optical remote sensing NDVI support vector machine classifier Kabini critical zone observatory

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Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

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BMC BIOINFORMATICS 2011年第s1期12卷 S47-S47页

作者： Huang, Hui-Lin Lin, I-Che Liou, Yi-Fan Tsai, Chia-Ta Hsu, Kai-Ti Huang, Wen-Lin Ho, Shinn-Jang Ho, Shinn-Ying Natl Chiao Tung Univ Dept Biol Sci & Engn Hsinchu Taiwan Natl Chiao Tung Univ Inst Bioinformat & Syst Biol Hsinchu Taiwan Asia Pacific Inst Creat Dept Multimedia Entertainment Sci Miaoli Taiwan Natl Formosa Univ Dept Automat Engn Yunlin 632 Taiwan

Background: Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results: This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van De

关键词： support vector machine Feature vector support vector machine classifier Binding Mechanism Orthogonal Experimental Design

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An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks

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COMPUTER NETWORKS 2020年 180卷 107390-107390页

作者： Kasim, Omer Kutahya Dumlupinar Univ Simav Technol Fac Dept Elect & Elect Engn TR-43500 Kutahya Turkey

The number of devices connected to the Internet is increasing day by day. This increase causes cyber-attacks to be larger and more complex. It is important to sdetect the anomalies rapidly when there is a cyber-attack. In detecting anomalies, high false positive rate is obtained by using feature extraction based on statistical calculations and machine learning algorithms. In proposed approach, the measured values obtained from the network are normalized between 0 and 1. These values applied to autoencoder model trained with optimum hyper parameters. This model contributes to feature learning and dimensional reduction. support vector machines effectively differentiate between normal and DDOS attack traffic by using these features. The CICIDS dataset and virtually generated DDOS traffic are used to validate the proposed approach and measure its performance. The results show that the proposed approach speeds up training and testing times and performs better classification performance metrics than most previous approaches. The novelty of the study is that AE-SVM trained with CICIDS successfully captures virtually generated DDOS traffic data. Despite the unbalanced data set, 99.1% test success was achieved in detection of DDOS traffic which is produced with Kali Linux. This success contributed to the solution of the high false-positive problem compared to other models.

关键词： DOS attack detection Normalization Autoencoder deep learning model support vector machine classifier

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Improving the performance of SVM-RFE to select genes in microarray data

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BMC BIOINFORMATICS 2006年第s2期7卷 S12-S12页

作者： Ding, Yuanyuan Wilkins, Dawn Univ Mississippi Dept Comp & Informat Sci University MS 38677 USA

Background: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. The effectiveness of the RFE algorithm is generally considered excellent, but the primary obstacle in using it is the amount of computational power required. Results: Here we introduce a variant of RFE which employs ideas from simulated annealing. The goal of the algorithm is to improve the computational performance of recursive feature elimination by eliminating chunks of features at a time with as little effect on the quality of the reduced feature set as possible. The algorithm has been tested on several large gene expression data sets. The RFE algorithm is implemented using a support vector machine to assist in identifying the least useful gene(s) to eliminate. Conclusion: The algorithm is simple and efficient and generates a set of attributes that is very similar to the set produced by RFE.

关键词： support vector machine Simulated Annealing Acute Lymphoblastic Leukemia support vector machine classifier Prediction Rate

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Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine

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BMC BIOINFORMATICS 2005年第1期6卷 310-310页

作者： Xue, CH Li, F He, T Liu, GP Li, YD Zhang, XG Tsinghua Univ Dept Automat MOE Key Lab Bioinformat Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China Univ Glamorgan Sch Elect Pontypridd CF37 1DL M Glam Wales

Background: MicroRNAs (miRNAs) are a group of short (similar to 22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology. Results: A set of novel features of local contiguous structure-sequence information is proposed for distinguishing the hairpins of real pre-miRNAs and pseudo pre-miRNAs. support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data. Remarkably, the SVM classifier built on human data can correctly identify up to 90% of the pre-miRNAs from other species, including plants and virus, without utilizing any comparative genomics information. Conclusion: The local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new miRNAs.

关键词： support vector machine support vector machine classifier Hairpin Structure miRNA Precursor Sequence Segment

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Determinants of antigenicity and specificity in immune response for protein sequences

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BMC BIOINFORMATICS 2011年第1期12卷 1-13页

作者： Wang, Yulong Wu, Wenjun Negre, Nicolas N. White, Kevin P. Li, Cheng Shah, Parantu K. Dana Farber Canc Inst Dept Biostat & Computat Biol Boston MA 02115 USA Harvard Univ Sch Publ Hlth Boston MA 02115 USA Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Univ Chicago Inst Genom & Syst Biol Chicago IL 60637 USA

Background: Target specific antibodies are pivotal for the design of vaccines, immunodiagnostic tests, studies on proteomics for cancer biomarker discovery, identification of protein-DNA and other interactions, and small and large biochemical assays. Therefore, it is important to understand the properties of protein sequences that are important for antigenicity and to identify small peptide epitopes and large regions in the linear sequence of the proteins whose utilization result in specific antibodies. Results: Our analysis using protein properties suggested that sequence composition combined with evolutionary information and predicted secondary structure, as well as solvent accessibility is sufficient to predict successful peptide epitopes. The antigenicity and the specificity in immune response were also found to depend on the epitope length. We trained the B-Cell Epitope Oracle (BEOracle), a support vector machine (SVM) classifier, for the identification of continuous B-Cell epitopes with these protein properties as learning features. The BEOracle achieved an F1-measure of 81.37% on a large validation set. The BEOracle classifier outperformed the classical methods based on propensity and sophisticated methods like BCPred and Bepipred for B-Cell epitope prediction. The BEOracle classifier also identified peptides for the ChIP-grade antibodies from the modENCODE/ ENCODE projects with 96.88% accuracy. High BEOracle score for peptides showed some correlation with the antibody intensity on Immunofluorescence studies done on fly embryos. Finally, a second SVM classifier, the B-Cell Region Oracle (BROracle) was trained with the BEOracle scores as features to predict the performance of antibodies generated with large protein regions with high accuracy. The BROracle classifier achieved accuracies of 75.2663.88% on a validation set with immunofluorescence, immunohistochemistry, protein arrays and western blot results from Protein Atlas database. Conclusions: Together our

关键词： support vector machine Classification Performance support vector machine classifier Solvent Accessibility Position Specific Score Matrix

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Automated breast cancer classification using near-infrared optical tomographic images

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JOURNAL OF BIOMEDICAL OPTICS 2008年第4期13卷 44001-44001页

作者： Wang, James Z. Liang, Xiaoping Zhang, Qizhi Fajardo, Laurie L. Jiang, Huabei Univ Florida J Crayton Pruitt Family Dept Biomed Engn Gainesville FL 32611 USA Clemson Univ Sch Comp Clemson SC 29634 USA Univ Iowa Dept Radiol Iowa City IA 52242 USA

An automated procedure for detecting breast cancer using near-infrared (NIR) tomographic images is presented. This classification procedure automatically extracts attributes from three imaging parameters obtained by an NIR imaging system. These parameters include tissue absorption and reduced scattering coefficients, as well as a tissue refractive index obtained by a phase-contrast-based reconstruction approach. A support vector machine (SVM) classifier is utilized to distinguish the malignant from the benign lesions using the automatically extracted attributes. The classification results of in vivo tomographic images from 35 breast masses using absorption, scattering, and refractive index attributes demonstrate high sensitivity, specificity, and overall accuracy of 81.8%, 91.7%, and 88.6% respectively, while the classification sensitivity, specificity, and overall accuracy are 63.6%, 83.3%, and 77.1%, respectively, when only the absorption and scattering attributes are used. Furthermore, the automated classification procedure provides significantly improved specificity and overall accuracy for breast cancer detection compared to those by an experienced technician through visual examination. (C) 2008 Society of Photo-Optical Instrumentation Engineers.

关键词： phase-contrast diffuse optical tomography refractive index image absorption coefficient image scattering coefficient image support vector machine classifier image segmentation feature extraction

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