Multiple Linear Support Vector Machine Research Articles

Classification of Large-Scale Datasets of Landsat-8 Satellite Image Based on LIBLINEAR Library

One of the most common machine learning approaches is linear classification. Multithreading used in LIBLINEAR Library has become an important research topic for processing large datasets. Working in a multithreaded environment accelerates the training of large datasets and increases classification accuracy. This study examines multiple linear support vector machine implementations for binary classification of Landsat-8 satellite image datasets to determine the optimal model. Four datasets have been created for different scenes in Iraq. Each dataset contains millions of pixels to be classified optimally. This article selects an appropriate algorithm for such a large dataset of current types in the LIBLINEAR library optimal for organizing large datasets using the linear SVM algorithm. Each dataset containing about 4.5 million samples was used to test the performance. The seven linear SVM techniques have statically examined the most effective SVM implementation method. Accuracy, F-Score, and Kappa are used as model assessment measures to evaluate and rank the models' performances. Based on the results, the LibLINEAR library with type 4 (LINLINEAR4) classifier was the best classifier for satellite image classification in remote sensing when applied to large datasets. The accuracy, Kappa, and f-score of the LIBLINEAR 4 classifier are as follows: (92.89 %), (73.53 %), and (77.77%) with dataset1, (96.25 %), (59.97 %), and (61.88%) with dataset2, (96.65%), (68.33%), and (70.03%), and (94.72%), (53.67%), and (56.25%), respectively, with dataset4.

Classification of Large-Scale Datasets of Landsat-8 Satellite Image Based on LIBLINEAR Library

One of the most common machine learning approaches is linear classification. Multithreading used in LIBLINEAR Library has become an important research topic for processing large datasets. Working in a multithreaded environment accelerates the training of large datasets and increases classification accuracy. This study examines multiple linear support vector machine implementations for binary classification of Landsat-8 satellite image datasets to determine the optimal model. Four datasets have been created for different scenes in Iraq. Each dataset contains millions of pixels to be classified optimally. This article selects an appropriate algorithm for such a large dataset of current types in the LIBLINEAR library optimal for organizing large datasets using the linear SVM algorithm. Each dataset containing about 4.5 million samples was used to test the performance. The seven linear SVM techniques have statically examined the most effective SVM implementation method. Accuracy, F-Score, and Kappa are used as model assessment measures to evaluate and rank the models' performances. Based on the results, the LibLINEAR library with type 4 (LINLINEAR4) classifier was the best classifier for satellite image classification in remote sensing when applied to large datasets. The accuracy, Kappa, and f-score of the LIBLINEAR 4 classifier are as follows: (92.89 %), (73.53 %), and (77.77%) with dataset1, (96.25 %), (59.97 %), and (61.88%) with dataset2, (96.65%), (68.33%), and (70.03%), and (94.72%), (53.67%), and (56.25%), respectively, with dataset4.

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Many organelles inside and outside a living cell depend on the perfect behavior of Golgi apparatus for smooth and normal functioning. Its poor performance may lead to many inheritable diseases like diabetes and cancer. Therefore, it is highly crucial to detect any strange behavior of Golgi apparatus in advance. Accurate discrimination of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cis</i> -Golgi from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trans</i> -Golgi proteins surely helps researchers identify the role of Golgi proteins in various diseases and assist pharmacists in drug development. In this work, various hybrid models of Bi-Profile Bayes, Bigram PSSM, Di-Peptide Composition, and Split Amphiphilic Pseudo Amino Acid Composition with SMOTE oversampling technique have been employed to discriminate Golgi protein types. Multiple linear Support Vector Machines have been used to exploit the discrimination power of these models. The proposed prediction system: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Golgi-predictor</i> has shown significant performance and achieved promising results compared to other existing state-of-the-art techniques. Through the 10-fold cross-validation, the proposed system achieved an accuracy value of 97.6%, sensitivity value of 98.8%, specificity value of 96.5%, G-mean value of 97.6%, MCC value of 0.95, and F-score value of 0.97. Similarly, through the jackknife cross-validation, the achieved values for accuracy, sensitivity, specificity, G-mean, MCC, and F-score are respectively, 96.5%, 97.8%, 95.2%, 96.4%, 0.93, and 0.96. Moreover, through the independent dataset testing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Golgi-predictor</i> demonstrated significant enhancement in performance over other techniques. The proposed methodology aims at supporting drug designers in pharmaceutical industry and assisting researchers from the fields of bioinformatics and computational biology towards better innovation in predicting the behavior of Golgi proteins.

A fast classification strategy for SVM on the large-scale high-dimensional datasets

The challenges of the classification for the large-scale and high-dimensional datasets are: (1) It requires huge computational burden in the training phase and in the classification phase; (2) it needs large storage requirement to save many training data; and (3) it is difficult to determine decision rules in the high-dimensional data. Nonlinear support vector machine (SVM) is a popular classifier, and it performs well on a high-dimensional dataset. However, it easily leads overfitting problem especially when the data are not evenly distributed. Recently, profile support vector machine (PSVM) is proposed to solve this problem. Because local learning is superior to global learning, multiple linear SVM models are trained to get similar performance to a nonlinear SVM model. However, it is inefficient in the training phase. In this paper, we proposed a fast classification strategy for PSVM to speed up the training time and the classification time. We first choose border samples near the decision boundary from training samples. Then, the reduced training samples are clustered to several local subsets through MagKmeans algorithm. In the paper, we proposed a fast search method to find the optimal solution for MagKmeans algorithm. Each cluster is used to learn multiple linear SVM models. Both artificial datasets and real datasets are used to evaluate the performance of the proposed method. In the experimental result, the proposed method prevents overfitting and underfitting problems. Moreover, the proposed strategy is effective and efficient.