AIMC Topic: Support Vector Machine

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Exploiting multi-layered vector spaces for signal peptide detection.

International journal of data mining and bioinformatics
Analysing and classifying sequences based on similarities and differences is a mathematical problem of escalating relevance and importance in many scientific disciplines. One of the primary challenges in applying machine learning algorithms to sequen...

A novel random forests-based feature selection method for microarray expression data analysis.

International journal of data mining and bioinformatics
High-dimensional data and a large number of redundancy features in bioinformatics research have created an urgent need for feature selection. In this paper, a novel random forests-based feature selection method is proposed that adopts the idea of str...

Ensemble of sparse classifiers for high-dimensional biological data.

International journal of data mining and bioinformatics
Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature...

Granular support vector machine to identify unknown structural classes of protein.

International journal of data mining and bioinformatics
To date, classification of structural class using local protein structure rather than the whole structure has been gaining widespread attention. It is noted that the structural class lies in local composition or arrangement of secondary structure, wh...

On mining incomplete medical datasets: Ordering imputation and classification.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: To collect medical datasets, it is usually the case that a number of data samples contain some missing values. Performing the data mining task over the incomplete datasets is a difficult problem. In general, missing value imputation can b...

Tuning to optimize SVM approach for assisting ovarian cancer diagnosis with photoacoustic imaging.

Bio-medical materials and engineering
Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of cla...

Application of multi-output support vector regression on EMGs to decode hand continuous movement trajectory.

Bio-medical materials and engineering
Applications of neural machine interfaces have received increased attention during the last decades. It is crucial to realize the continuous control of prosthetic devices based on biological signals. In order to deal with the highly nonlinear relatio...

Classification of imbalanced bioinformatics data by using boundary movement-based ELM.

Bio-medical materials and engineering
To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution ...

A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies with support vector machines.

Bio-medical materials and engineering
G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of ma...

Improve the diagnosis of atrial hypertrophy with the local discriminative support vector machine.

Bio-medical materials and engineering
Computer-aided diagnosis (CAD) approaches succeed in detecting a number of diseases, however, they are not good at addressing atrial hypertrophy disease due to the lack of training data. Support Vector Machine (SVM) is very popular in few CAD solutio...