Defect Prediction for Object Oriented Software using Support Vector based Fuzzy Classification Model

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

Download Full-text

Fuzzy One-Class Classification Model Using Contamination Neighborhoods

Advances in Fuzzy Systems ◽

10.1155/2012/984325 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Lev V. Utkin

Keyword(s):

Risk Measures ◽

Synthetic Data ◽

Fuzzy Model ◽

Fuzzy Classification ◽

Classification Model ◽

Support Vector ◽

Standard Support Vector Machine ◽

Robust Model ◽

Data Points ◽

One Class Classification

A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.

Download Full-text

Probabilistic Support Vector Regression Classification Model for Credit Card Fraud Detection

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i9.840843 ◽

2018 ◽

Vol 6 (9) ◽

pp. 840-843

Author(s):

Nikita Sawhney ◽

B. Kaur ◽

H. Kaur

Keyword(s):

Support Vector Regression ◽

Credit Card ◽

Fraud Detection ◽

Classification Model ◽

Support Vector ◽

Credit Card Fraud ◽

Probabilistic Support

Download Full-text

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1770 ◽

2020 ◽

Vol 4 (2) ◽

pp. 329-335

Author(s):

Rusydi Umar ◽

Imam Riadi ◽

Purwono

Keyword(s):

Social Media ◽

Gradient Descent ◽

Classification Model ◽

Stochastic Gradient Descent ◽

Support Vector ◽

Svm Algorithm ◽

Vector Machines ◽

Performance Patterns ◽

A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Download Full-text

Drug Target Group Prediction with Multiple Drug Networks

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666190702103927 ◽

2020 ◽

Vol 23 (4) ◽

pp. 274-284 ◽

Cited By ~ 12

Author(s):

Jingang Che ◽

Lei Chen ◽

Zi-Han Guo ◽

Shuaiqun Wang ◽

Aorigele

Keyword(s):

Drug Target ◽

Low Cost ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector ◽

Multiple Drug ◽

Property A ◽

Multiple Networks ◽

Proposed Model ◽

The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

Download Full-text

Predicting Hub Genes of Glioblastomas Based on Support Vector Machine Combined with CFS algorithms

Current Bioinformatics ◽

10.2174/1574893615999200819162140 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chun Qiu ◽

Sai Li ◽

Shenghui Yang ◽

Lin Wang ◽

Aihui Zeng ◽

...

Keyword(s):

Support Vector Machine ◽

Expression Profiles ◽

Independent Set ◽

Classification Model ◽

Support Vector ◽

Feature Subset ◽

Hub Genes ◽

Effective Prevention ◽

Key Genes ◽

Control Samples

Aim: To search the genes related to the mechanisms of the occurrence of glioma and to try to build a prediction model for glioblastomas. Background: The morbidity and mortality of glioblastomas are very high, which seriously endangers human health. At present, the goals of many investigations on gliomas are mainly to understand the cause and mechanism of these tumors at the molecular level and to explore clinical diagnosis and treatment methods. However, there is no effective early diagnosis method for this disease, and there are no effective prevention, diagnosis or treatment measures. Methods: First, the gene expression profiles derived from GEO were downloaded. Then, differentially expressed genes (DEGs) in the disease samples and the control samples were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, the correlation-based feature subset (CFS) method was applied to the selection of key DEGs. In addition, the classification model between the glioblastoma samples and the controls was built by an Support Vector Machine (SVM) based on selected key genes. Results and Discussion: Thirty-six DEGs, including 17 upregulated and 19 downregulated genes, were selected as the feature genes to build the classification model between the glioma samples and the control samples by the CFS method. The accuracy of the classification model by using a 10-fold cross-validation test and independent set test was 76.25% and 70.3%, respectively. In addition, PPP2R2B and CYBB can also be found in the top 5 hub genes screened by the protein– protein interaction (PPI) network. Conclusions: This study indicated that the CFS method is a useful tool to identify key genes in glioblastomas. In addition, we also predicted that genes such as PPP2R2B and CYBB might be potential biomarkers for the diagnosis of glioblastomas.

Download Full-text

Comparison of Spectroscopic Techniques Combined with Chemometrics for Cocaine Powder Analysis

Journal of Analytical Toxicology ◽

10.1093/jat/bkaa101 ◽

2020 ◽

Vol 44 (8) ◽

pp. 851-860

Author(s):

Joy Eliaerts ◽

Natalie Meert ◽

Pierre Dardenne ◽

Vincent Baeten ◽

Juan-Antonio Fernandez Pierna ◽

...

Keyword(s):

Gas Chromatography ◽

Near Infrared ◽

Evaluation Criteria ◽

Classification Model ◽

Support Vector ◽

Spectroscopic Techniques ◽

Data Set ◽

Promising Tool ◽

Powder Analysis ◽

Mir Spectra

Abstract Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC–MS) and gas chromatography with flame-ionization detection (GC–FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

Download Full-text

Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽

10.3390/agriculture11040371 ◽

2021 ◽

Vol 11 (4) ◽

pp. 371

Author(s):

Yu Jin ◽

Jiawei Guo ◽

Huichun Ye ◽

Jinling Zhao ◽

Wenjiang Huang ◽

...

Keyword(s):

Random Forest ◽

Satellite Imagery ◽

Feature Space ◽

Kappa Coefficient ◽

Classification Model ◽

Support Vector ◽

Textural Feature ◽

Monitoring Accuracy ◽

Areca Catechu ◽

High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

Download Full-text

Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽

10.3390/medicina57060527 ◽

2021 ◽

Vol 57 (6) ◽

pp. 527

Author(s):

Vijay Vyas Vadhiraj ◽

Andrew Simpkin ◽

James O’Connell ◽

Naykky Singh Singh Ospina ◽

Spyridoula Maraka ◽

...

Keyword(s):

Decision Making ◽

Sensitivity And Specificity ◽

Thyroid Nodules ◽

Median Filter ◽

Ultrasound Image ◽

Multiple Instance Learning ◽

Image Features ◽

Classification Model ◽

Support Vector ◽

Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

Download Full-text

Forensic analysis of beverage stains using hyperspectral imaging

Scientific Reports ◽

10.1038/s41598-021-85737-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Binu Melit Devassy ◽

Sony George

Keyword(s):

Hyperspectral Imaging ◽

Crime Scene ◽

Forensic Analysis ◽

Classification Model ◽

Support Vector ◽

Additional Information ◽

Non Invasive ◽

Spectral Bands ◽

Data Dimensionality Reduction ◽

Gradient Based

AbstractDocumentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.

Download Full-text