Analysis of support vector machines

Dimensionality reduction is one of the key issues of machine learning and data mining, especially for high-dimensional data set. In the literature, there are various dimensionality reduction methods, such as PCA, LDA, and KLDA, and the difference between them mainly lies in the optimization objective. In this paper, we propose a new dimensionality reduction method, whose optimization objective is to maximize the margin between different classes, after projecting the original features into some specific lower-dimensional subspace. The specific subspace is constructed with the help of soft margin support vector machines. Our experiments based on several real-world datasets show that this method improves the performance on classification, and it not only can reduce redundant information in features but also is robust to noise.

Deep convolutional cross-connected kernel mapping support vector machine based on SelectDropout

Support Vector Machine (SVM) is a fundamental technique in machine learning. A long time challenge facing SVM is how to deal with outliers (caused by mislabeling), as they could make the classes in SVM nonseparable. Existing techniques, such as soft margin SVM, ν-SVM, and Core-SVM, can alleviate the problem to certain extent, but cannot completely resolve the issue. Recently, there are also techniques available for explicit outlier removal. But they suffer from high time complexity and cannot guarantee quality of solution. In this paper, we present a new combinatorial approach, called Random Gradient Descent Tree (or RGD-tree), to explicitly deal with outliers; this results in a new algorithm called RGD-SVM. Our technique yields provably good solution and can be efficiently implemented for practical purpose. The time and space complexities of our approach only linearly depend on the input size and the dimensionality of the space, which are significantly better than existing ones. Experiments on benchmark datasets suggest that our technique considerably outperforms several popular techniques in most of the cases.

Speech recognition systems aim to make human-machine communication quickly and easily. In recent years, various researches and studies have been carried out to develop speech recognition systems. Examples of these studies are speech recognition, speaker recognition and speaker verification. In this study, speech recognition systems were investigated, methods used in the literature were investigated and a Turkish speech recognition application was developed. The application consists of speech coding and speech recognition. Firstly 20 Turkish words which are frequently used on the computer were determined. There are 20 records from each word. A total of 400 words were recorded on the computer with a microphone. In the speech coding section of the application, these words recorded on the computer are encoded by the Linear Pre-estimation Coding (LPC) method and the LPC parameters for each word are obtained. In the speech recognition section of the application, the Support Vector Machines (SVM) method is used. Two types of SVM classifiers are designed. These are the Soft Margin SVM (SM-SVM) classifier and the Least Square SVM (LS-SVM) classifier. Classification consists of training and testing stages. Of the 400 coded words, 200 were used for the training phase and 200 were used for the testing phase. As a result, 91% accurate recognition success for the SM-SVM classifier; 71% correct recognition of the LS-SVM classifier has been achieved.

Geometrical interpretation and applications of membership functions with fuzzy rough sets

Power purchase agreements aim to secure long-term contracts between sellers and buyers, particularly in renewable energy transactions. However, successful negotiations for a fixed long-term price and energy volume while ensuring maximum utility for stakeholders remains a significant challenge. This paper introduces a comprehensive model for negotiating 24/7 power purchase agreements, focusing on hourly pricing to address deficits and surpluses throughout each day of the contract timeline. The model incorporates demand flexibility through battery storage, settling on the strike price using Nash Bargaining theory and optimal management of energy consumption relative to market price fluctuations. A soft margin support vector machine classification model determines the buyer’s maximum acceptable price. Moreover, Gaussian process classification is employed to calculate a probabilistic, risk-adjusted strike price, enabling a data-driven approach to power purchase negotiations. The proposed model’s performance is demonstrated through a detailed case study of Norway, illustrating how demand flexibility can significantly lower long-term power purchase agreement contract prices. The analysis of yearly price trends indicates that incorporating flexibility resources in long-term energy contracts may lead to a reduction in strike prices by around 25%. Moreover, such flexibility enhances demand-generation matching, thereby increasing renewable energy transactions within such agreements.

This paper presents two new types of support vector machine (SVM) algorithms, one is based on Hard-margin SVM and the other is based on Soft-margin SVM. These algorithms can handle data with tolerance of which the concept includes some errors, ranges or missing values in data. First, the concept of tolerance is introduced into optimization problems of Support Vector Machine. Second, the optimization problems with the tolerance are solved by using the Karush-Kuhn-Tucker conditions. Next, new algorithms are constructed based on the unique and explicit optimal solutions of the optimization problem. Finally, the effectiveness of the proposed algorithms is verified through some numerical examples for the artificial data.

Comparing accuracy of machine learning approaches to identifying parathyroid adenomas: Lessons and new directions

Covid-19 is disease caused by the new corona virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The effect of this virus usually causes infection on respiratory system. Covid-19 was rapidly spread globally. Experts said that the factor that caused this to spread rapidly is human mobility. Therefore, several countries create new rules so that it can suppress the spreading of this disease, by prohibiting a large scale gathering, keeping away distance with each other, mandatory rule of using mask, and the prohibition for the entry of their country. This research proposes a performance analysis of Support Vector Machine (SVM) to classify the monthly data of covid-19. The data used in this research is a series of covid-19 data of towns in Bandung from November 2020 until December 2021. From conducting this research It is found that the best accuracy was found on December 2021 with the accuracy of 100%, followed by July and August with the accuracy of 97%, and October with the value of 90%. We can conclude that Support Vector Machine (SVM), is a good method on classifying the monthly covid-19 data.

Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions.

The paper will present the original NBV (Nearest Boundary Vector) classifier whose structure has been inspired by the structure of CP (Counter Propagation) neural network, which uses the methods applied in the minimum-distance classification while in its operation drawn on the idea of functioning of SVM (Support Vector Machines) classifiers. The classification algorithm which is used by it relies on the original concept of a set of Boundary Vectors. It is characterized by the possibility of creation of various shapes of decision-making regions and it enables effective multi-class recognition. Recognition efficiency of NBV classifier will be confronted with efficiency of SVM classifiers.

We describe a novel application of support vector machines and multiscale texture and color invariants to a problem in biological oceanography: the identification of 6 species of bivalve larvae. Our data consists of polarized color images of scallop and other bivalve larvae (between 2 and 17 days old) collected from the ocean by a shipboard optical imaging system of our design. Larvae of scallops, clams, and oysters are small (100 microns) with few distinguishing features when observed under standard light microscopy. However, the use of polarized light with a full wave retardation plate produces a vivid color, birefringence pattern. The patterns display very subtle differences between species, often not discernable to human observers. We show that a soft-margin support vector machine with Gaussian RBF kernel is a good discriminator on a feature set extracted from Gabor wavelet transforms and color distribution angles of each image. By constraining the Gabor center frequencies to be low, the resulting system can attain classification accuracy in excess of 90% for vertically oriented images, and in excess of 80% for randomly oriented images.

BackgroundSeveral models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models.Methods352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (± SE).ResultsThe area under ROC curve for the MLR and SVM in the validation data set were 0.768 (± 0.04) vs. 0.802 (± 0.04) in the first model (p = 0.19) and 0.781 (± 0.05) vs. 0.808 (± 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively.ConclusionThe discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies.

This study presents a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithms for sentiment analysis of TikTokShop application user reviews. As TikTokShop emerges as an innovative platform integrating social media with e-commerce, understanding user sentiments becomes crucial for both consumers and businesses. A balanced dataset of 3,000 user reviews (1,000 positive, 1,000 neutral, and 1,000 negative) was collected through web scraping from Google Play Store. Following comprehensive preprocessing including cleansing, case folding, normalization, tokenization, stopword removal, and stemming, the data was vectorized using TF-IDF. Performance evaluation utilized accuracy, precision, recall, F1-score, confusion matrix, and 10-fold cross-validation. Results demonstrate that SVM consistently outperformed Naïve Bayes with higher accuracy (68.86% vs. 64.48%), precision (68.43% vs. 64.19%), and F1-score (68.58% vs. 62.46%). SVM exhibited balanced classification across all sentiment categories, while Naïve Bayes excelled at identifying negative sentiments (94.1% accuracy) but struggled significantly with neutral reviews (38.5%). Despite SVM's superior performance, Naïve Bayes demonstrated remarkable computational efficiency, with training time 224 times faster than SVM. The study reveals complementary strengths between the algorithms, suggesting potential value in ensemble approaches. These findings contribute to the understanding of sentiment analysis in video-based e-commerce platforms and provide valuable insights for businesses seeking to leverage user feedback for improved decision-making.

The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals. Effective connectivity features were extracted from electroencephalogram (EEG) to analyze relationships between regions.Four feature selection methods used to select most significant effective features.Support vector machine (SVM) used for discrimination of right and left hand motor imagery (MI) task. In this study, we investigated brain activity using effective connectivity during MI task based on EEG signals. The motor imagery task can accomplish the same goal as motor execution, since they are both activated by the same brain area. Transfer entropy, coherence, and Granger casualty were employed to extract the features. Differential patterns of activity between the left vs. right MI task showed activity around the motor area rather than other areas. In order to reduce redundant information and select the most significant effective connectivity features, four feature subset selection algorithms are used: Relief-F, Fisher, Laplacian, and learning-based clustering feature selection (LLCFS). Then, support vector machine (SVM) and linear discriminant analysis (LDA) are used to classify left and right hand MI task. Comparison of three different connectivity methods showed that TE index had the highest classification accuracy, and could be useful for the discrimination of right and left hand MI tasks from multichannel EEG signals.