Classification Models for HERG Potassium Channel Inhibitors Based on the Support Vector Machine Approach

2009 ◽  
Vol 25 (08) ◽  
pp. 1581-1586 ◽  
Author(s):  
LI Ping ◽  
◽  
◽  
TAN Ning-Xin ◽  
RAO Han-Bing ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Potassium Channel ◽  
Support Vector ◽  
Classification Models

scholarly journals Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

2021 ◽  
Author(s):  
Lance F Merrick ◽  
Dennis N Lozada ◽  
Xianming Chen ◽  
Arron H Carter
Keyword(s):  
Support Vector Machine ◽  
Winter Wheat ◽  
Genomic Selection ◽  
Stripe Rust ◽  
Regression Models ◽  
Prediction Models ◽  
Support Vector ◽  
Classification Models ◽  
Breeding Lines ◽  
Classification And Regression

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in four years (2016-2018, and 2020) and a diversity panel phenotyped in four years (2013-2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using rrBLUP and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Further, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

Classification study of solvation free energies of organic molecules using machine learning techniques

RSC Advances ◽  
2014 ◽  
Vol 4 (106) ◽  
pp. 61624-61630 ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Silvia A. Martins ◽  
Sergio F. Sousa ◽  
Maria J. Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Organic Molecules ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Models ◽  
Free Energies ◽  
Learning Techniques ◽  
Solvation Free Energies

Classification models to predict the solvation free energies of organic molecules were developed using decision tree, random forest and support vector machine approaches and with MACCS fingerprints, MOE and PaDEL descriptors.

scholarly journals Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Na Liu ◽  
Jiang Shen ◽  
Man Xu ◽  
Dan Gan ◽  
Er-Shi Qi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Treatment Plan ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Classification Models ◽  
Misclassification Costs

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.

scholarly journals A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1153
Author(s):  
Gamaliel Simanungkalit ◽  
Jamie Barwick ◽  
Frances Cowley ◽  
Robin Dobos ◽  
Roger Hegarty
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Beef Cattle ◽  
Superior Performance ◽  
Support Vector ◽  
Controlled Environment ◽  
Nearest Neighbour ◽  
Classification Models ◽  
Licking Behaviour ◽  
Supplement Intake

Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.

scholarly journals LAND USE AND LAND COVER CLASSIFICATION USING HYPERSPECTRAL IMAGERY: EVALUATING THE PERFORMANCE OF SPECTRAL ANGLE MAPPER, SUPPORT VECTOR MACHINE AND RANDOM FOREST

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1841-1847 ◽  
Author(s):  
L. E. Christovam ◽  
G. G. Pessoa ◽  
M. H. Shimabukuro ◽  
M. L. B. T. Galo
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Principal Components ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Spectral Angle Mapper ◽  
Spectral Angle

<p><strong>Abstract.</strong> Land Use and Land Cover (LULC) information is an important data source for modeling environmental variables, so it is essential to develop high quality LULC maps. The hundreds of continuous spectral bands gathered with hyperspectral sensors provide high spectral detail and consequently confirm hyperspectral remote sensing as an appropriate option for many LULC applications. Despite increased spectral detail, issues like high dimensionality, huge volume of data and redundant information, mean that hyperspectral image classification is a complex task. It is therefore essential to develop classification approaches that deals with these issues. Since classification results are directly dependent on the dataset used, it is fundamental to compare and validate the classification approaches in public datasets. With this in mind, aiming to provide a baseline, four classification models in the relatively new hyperspectral HyRANK dataset were evaluated. The classification models were defined with three well-known classification algorithms: Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Random Forest (RF). A classification model with SAM and another with RF were defined with the 176 surface reflectance bands. A dimensionality reduction with principal component analysis was carried out and a classification model with SVM and another with RF were defined using 14 principal components as features. The results show that SVM and RF algorithms outperformed by far the SAM in terms of accuracy, and that the RF is slightly better than the SVM in this respect. It is also possible to see from the results that the use of principal components as features provided an improvement in the accuracy of the RF and an improvement of 28% in the time spent fitting the classification model.</p>

scholarly journals Support Vector Machine Based Feature Extraction For Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Face Recognition ◽  
Support Vector ◽  
Gender Recognition ◽  
Classification Models ◽  
Lasso Regression ◽  
Scale Invariant ◽  
And Gender ◽  
Blind Persons

Abstract Object detection and gender recognition were two different categories to be classified in a single section is a complicated task and this approach helps in supporting the blind people for an artificial vision. In this paper, our method to the betters vision sensation of blind persons by conversion of visualized data to audio data. Therefore this artificial intelligence model helps in detecting the objects as well as human face recognition with gender classification based on face recognition approach. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale-invariant feature transform(MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification, three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognizing human actions using image processing approach. This approach can be applied for both offline and online modes. But in this scenario, an offline mode was implemented and was tested with a combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression(LR) and LASSO regression were implemented. The final classification results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and proposed approach(LRGS) with 98.4% accurate detection rate with prediction name of classes.

Machine Learning Based Classification Models for Diagnosis of Diabetes

2021 ◽  
Vol 14 ◽  
Author(s):  
Sushma Jaiswal ◽  
Tarun Jaiswal
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Support Vector Machine ◽  
Expert System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fuzzy Expert System ◽  
Classification Models

Introduction: The expansion of an actual diabetes judgement structure by the fascinating improvement of computational intellect is observed as a chief objective currently. Numerous tactics based on the artificial network and machine-learning procedures have been established and verified alongside diabetes datasets, which remained typically associated with the entities of Pima Indian derivation. Nevertheless, extraordinary accuracy up to 99-100% in forecasting the precise diabetes judgement, none of these methods has touched scientific presentation so far. Various tools such as Machine Learning (ML) and Data Mining are used for correct identification of diabetes. These tools improve the diagnosis process associated with T2DM. Diabetes mellitus type 2 (DMT2) is a major problem in several developing countries but its early diagnosis can provide enhanced treatment and can save several people life. Accordingly, we have to develop a structure that diagnoses type 2 diabetes. In this paper, a fuzzy expert system is proposed that present the Mamdani fuzzy inference structure (MFIS) to diagnose type 2 diabetes meritoriously. For necessary evaluation of the proposed structure, a proportional revision has been originated, that provide the anticipated structure with Machine Learning algorithms, specifically J48 Decision-tree (DT), multilayer perceptron (MLP), support-vector-machine (SVM), and Naïve- Bayes (NB), fusion and mixed fusion-based methods. The advanced fuzzy expert system (FES) and the machine learning algorithms are authenticated with actual data commencing the UCI machine learning datasets. Furthermore, the concert of the fuzzy expert structure is appraised by equating it to connected work that used the MFIS to detect the occurrence of type 2 diabetes. Objective: This survey paper presents a review of recent advances in the area of machine learning based classification models for diagnosis of diabetes. Methods: This paper presents an extensive work done in the field of machine learning based classification models for diagnosis of type 2 diabetes where modified fusion of machine learning methods are compared to the basic models i.e. Radial basis function, K-nearest neighbor, support vector machine, J48, logistic regression, classification and regression tress etc. based on training and testing. Results: Fig. 3 and Fig. 4 summarizes the result based on prediction accurateness for each classifier of training and testing. Conclusion: The fuzzy expert system is the best among its rival classifiers; SVM performs very poorly with a very low true positive rate, i.e. a very high number of positive cases misclassified as (Non-diabetic) negative. Based on the evaluation it is clear that the fuzzy expert system has the highest precision value. However, J48 is the least accurate classifier. It has the highest number of false positives relative to the other classifiers mentioned in the testing part. The results show that the fuzzy expert system has the uppermost cost for both precision and recall. Thus, it has the uppermost value for F-measure in the training and testing datasets. J48 is considered the second-best classifier for the training dataset, whereas Naïve Bayes comes in the second rank in the testing dataset.

Human Activity Recognition Using The Human Skeleton Provided by Kinect

2021 ◽  
Vol 17 (2) ◽  
pp. 183-189
Author(s):  
Heba Salim ◽  
Musaab Alaziz ◽  
Turki Abdalla
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Human Body ◽  
Support Vector ◽  
Classification Models ◽  
Kinect Sensor ◽  
People Tracking ◽  
Human Skeleton ◽  
Average Accuracy ◽  
Svm Model

In this paper, a new method is proposed for people tracking using the human skeleton provided by the Kinect sensor, Our method is based on skeleton data, which includes the coordinate value of each joint in the human body. For data classification, the Support Vector Machine (SVM) and Random Forest techniques are used. To achieve this goal, 14 classes of movements are defined, using the Kinect Sensor to extract data containing 46 features and then using them to train the classification models. The system was tested on 12 subjects, each of whom performed 14 movements in each experiment. Experiment results show that the best average accuracy is 90.2 % for the SVM model and 99 % for the Random forest model. From the experiments, we concluded that the best distance between the Kinect sensor and the human body is one meter.

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