scholarly journals Approche de sélection d’attributs pour la classification basée sur l’algorithme RFE-SVM

2014 ◽  
Vol Volume 17 - 2014 - Special... ◽  
Author(s):  
Yahya Slimani ◽  
Mohamed Amir Essegir ◽  
Mouhamadou Lamine Samb ◽  
Fodé Camara ◽  
Samba Ndiaye
Keyword(s):  
Optimization Methods ◽  
Research Field ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Hybrid Approaches ◽  
Alternative Approach ◽  
Svm Algorithm ◽  
International Audience ◽  
Selection For ◽  
Active Research

International audience The feature selection for classification is a very active research field in data mining and optimization. Its combinatorial nature requires the development of specific techniques (such as filters, wrappers, genetic algorithms, and so on) or hybrid approaches combining several optimization methods. In this context, the support vector machine recursive feature elimination (SVM-RFE), is distinguished as one of the most effective methods. However, the RFE-SVM algorithm is a greedy method that only hopes to find the best possible combination for classification. To overcome this limitation, we propose an alternative approach with the aim to combine the RFE-SVM algorithm with local search operators based on operational research and artificial intelligence.

Mining Hidden Association Rules from Real-Life Data

2010 ◽  
pp. 168-184
Author(s):  
Marco-Antonio Balderas Cepeda
Keyword(s):  
Association Rules ◽  
Real Life ◽  
Research Field ◽  
Good Reliability ◽  
Highly Active ◽  
Network Intrusion ◽  
Real Life Data ◽  
Alternative Approach ◽  
Rule Sets ◽  
Active Research

Association rule mining has been a highly active research field over the past decade. Extraction of frequency-related patterns has been applied to several domains. However, the way association rules are defined has limited people’s ability to obtain all the patterns of interest. In this chapter, the authors present an alternative approach that allows us to obtain new kinds of association rules that represent deviations from common behaviors. These new rules are called anomalous rules. To obtain such rules requires that we extract all the most frequent patterns together with certain extension patterns that may occur very infrequently. An approach that relies on anomalous rules has possible application in the areas of counterterrorism, fraud detection, pharmaceutical data analysis and network intrusion detection. They provide an adaption of measures of interest to our anomalous rule sets, and we propose an algorithm that can extract anomalous rules as well. Their experiments with benchmark and real-life datasets suggest that the set of anomalous rules is smaller than the set of association rules. Their work also provides evidence that our proposed approach can discover hidden patterns with good reliability.

K-Anonymization-Based Temporal Attack Risk Detection Using Machine Learning Paradigms

2020 ◽  
pp. 2150050
Author(s):  
P. Geetha ◽  
Chandrakant Naikodi ◽  
L. Suresh
Keyword(s):  
Personal Data ◽  
Research Field ◽  
Data Publishing ◽  
Support Vector ◽  
Published Data ◽  
Ensemble Classifiers ◽  
Data Preservation ◽  
Risk Detection ◽  
Sequential Release ◽  
Active Research

Huge amount of personal data is collected by online applications and its protection based on privacy has brought a lot of major challenging issues. Hence, the [Formula: see text]-anonymization with privacy-preserving data publishing has emerged as an active research field. The published data contains personalized information, which may be used for analysis converting it to useful information. In this paper, Quasi identifier (QI) data publishing with data preservation through the [Formula: see text]-anonymization process is proposed. Moreover, the risks such as the temporal attack in the previous release of re-identifying QI information are evaluated using the [Formula: see text]-anonymity model. The development of independent and ensemble classifiers for finding efficient QI’s to avoid temporal attacks is the major objective of this paper. Therefore, the classifiers like Naïve Bayes, Support Vector Machine, and Multilayer Perceptron are used as base classifiers. An ensemble model based on these base classifiers is also used. The experimental results demonstrate that, the proposed classification approach is an effective K-anonymity tool for the enhancement of sequential release.

scholarly journals Mining Key Skeleton Poses with Latent SVM for Action Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoqiang Li ◽  
Yi Zhang ◽  
Dong Liao
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Research Field ◽  
Superior Performance ◽  
Support Vector ◽  
Depth Sensors ◽  
Latent Svm ◽  
Active Research ◽  
Simple Action

Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed commodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or use trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and simple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative skeleton poses, named as key skeleton poses. The pairwise relative positions of skeleton joints are used as feature of the skeleton poses which are mined with the aid of the latent support vector machine (latent SVM). The advantage of our method is resisting against intraclass variation such as noise and large nonlinear temporal deformation of human action. We evaluate the proposed approach on three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UTKinect Action dataset, and Florence 3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to the state-of-the-art skeleton-based action recognition methods.

Vehicle Detection Method for Intelligent Vehicle at Night Time Based on Video and Laser Information

2017 ◽  
Vol 32 (04) ◽  
pp. 1850009 ◽  
Author(s):  
Rong-Hui Zhang ◽  
Feng You ◽  
Fang Chen ◽  
Wen-Qiang He
Keyword(s):  
Gabor Filter ◽  
Region Growing ◽  
Vehicle Detection ◽  
Research Field ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Night Time ◽  
Video Images ◽  
Detection Technology ◽  
Svm Algorithm

Front vehicle detection technology is one of the hot spots in the advanced driver assistance system research field. This paper puts forward a method for front vehicles detection based on video-and-laser-information at night. First of all, video images and laser data are pre-processed with the region growing and threshold area expunction algorithm. Then, the features of front vehicles are extracted by use of a Gabor filter based on the uncertainty principle, and the distances to front vehicles are obtained through laser point cloud. Finally, front vehicles are automatically classified during identification with the improved sequential minimal optimization algorithm, which was based on the support vector machine (SVM) algorithm. According to the experiment results, the method proposed by this text is effective and it is reliable to identify vehicles in front of intelligent vehicles at night.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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

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.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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