Support Vector Machine Cross Validation Research Articles

Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

Abstract
 
 Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The Klik Indomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting information on consumer’s opinion or review is not an easy task and need a specific method in categorizing or grouping these reviews into certain groups, i.e. positive or negative reviews. The sentiment analysis study of a review application in GooglePlay is still rare. Therefore, this paper analysis the customer’s sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature Selection (FS) using the Particle Swarm Optimization method. The results for NB without using FS optimization were 69.74% for accuracy and 0.518 for Area Under Curve (AUC) and for SVM without using FS optimization were 81.21% for accuracy and 0.896 for AUC. While the results of cross-validation NB with FS are 75.21% for accuracy and 0.598 for AUC and cross-validation of SVM with FS is 81.84% for accuracy and 0.898 for AUC, while there is an increase when using the Feature Selection (FS) Particle Swarm Optimization and also the modeling algorithm SVM has a higher value compared to NB for the dataset used in this study.
 
 Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.

Aflatoxin contaminated degree detection by hyperspectral data using band index

Aflatoxin is a highly toxic and carcinogenic substance with fluorescence characteristic. To explore the feasibility of detection the degree of aflatoxin contamination using hyperspectral imaging technology, we proposed a machine learning detection method based on support vector machine (SVM) combining band index and narrow band. First, five concentrations of aflatoxin solutions (10ug/L, 20ug/L, 50ug/L, 100ug/L and 10 mg/L) were prepared and dripped onto the surface of different peanut kernels. Next, hyperspectral images with 33 bands (400–720 nm) were acquired for each sample using a hyperspectral imaging system under 365 nm ultraviolet (UV) light. Then four fluorescence indexes including Radiation Index (RI), Difference Radiation Indexes (DRI), Ratio Radiation Index (RRI) and Normalized Difference Radiation Index (NDRI) were proposed. Finally, Fisher method was used to optimize and obtain a narrowband spectrum, and RBF-SVM model was used to recognize aflatoxin and make regression analysis on the degree of aflatoxin contamination. Experimental results showed that DRI index had the optimal performance, and the accuracy rate of 5-fold cross validation of SVM were 95.5% and the mean square error (MSE) and correlation coefficient R were respectively 0.0223 and 0.9785. Results of this paper are of positive significance for the online aflatoxin detection and grading of agricultural products.

Granularity selection for cross-validation of SVM

Granularity selection is fundamental to granular computing. Cross-validation (CV) is widely adopted for model selection, where each fold of data set of CV can be considered as an information granule, and the larger the number of the folds is, the smaller the granularity of each fold is. Therefore, for CV, granularity selection is equal to the selection of the number of folds. In this paper, we explore the granularity selection for CV of support vector machine (SVM). We first use the Huber loss to smooth the hinge loss used in SVM, and to approximate CV of SVM. Then, we derive a tight upper bound of the discrepancy between the original and the approximate CV with a high convergence rate. Finally, based on this derived tight bound, we present a granularity selection criterion for trading off the accuracy and time cost. Experimental results demonstrate that the approximate CV with the granularity selection criterion gives the similar accuracies as the traditional CV, and meanwhile significantly improves the efficiency.

The Proximal Trajectory Algorithm in SVM Cross Validation.

We propose a bilevel cross-validation scheme for support vector machine (SVM) model selection based on the construction of the entire regularization path. Since such path is a particular case of the more general proximal trajectory concept from nonsmooth optimization, we propose for its construction an algorithm based on solving a finite number of structured linear programs. Our methodology, differently from other approaches, works directly on the primal form of SVM. Numerical results are presented on binary data sets drawn from literature.

Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder.

Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment. We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject. DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters. Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata. It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.