Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique

2011 ◽  
Vol 403-408 ◽  
pp. 3724-3728
Author(s):  
Chantima Ekwong ◽  
Sageemas Na Wichain ◽  
Choochart Haruechaiyasak
Keyword(s):  
Quality Assessment ◽  
Vocational Education ◽  
Hierarchical Classification ◽  
Unstructured Data ◽  
Classification Model ◽  
Support Vector ◽  
Education Standards ◽  
National Education ◽  
Vector Machines

According to the laws of education in Thailand, the Office for National Education Standards and Quality Assessment is responsible for assessing the external educational institutes in order to develop the quality and educational standards. The external quality assessment reports are represented in both structured and unstructured data. In this paper, we focus on the analysis of unstructured data, i.e., to automatically classify strength and weakness points. We propose and evaluate two different classification models: Flat Classification and Hierarchical Classification. Three algorithms, Naive Bayes, Support Vector Machines (SVM) and Decision Tree, were used in the experiments. The results showed that classification viathe Hierarchical Classification model by using the SVM yielded the best performance. The classification of strength and weakness points yielded the F-measure equal to 0.843 and 0.893, respectively. The proposed approach can be applied as a decision support function for quality assessment in vocational education.

scholarly journals Modelos de aprendizaje automático para el apoyo en la clasificación de tipos de cáncer a partir de datos estructurados y no estructurados de expedientes clínicos

2020 ◽  
Author(s):  
◽  
Erick Esteven Montelongo González
Keyword(s):  
Unstructured Data ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Multi Layer Perceptron ◽  
Health Area ◽  
Vector Machines ◽  
Clinical Records ◽  
Unstructured Information

The existence of large volumes of data generated by the health area presents an important opportunity for analysis. This can obtain information to support physicians in the decisionmaking process for the diagnosis or treatment of diseases, such as cancer. The present work shows a methodology for the classification of patients with liver, lung and breast cancer, through machine learning models, to obtain the model that performs best in the classification. The methodology considers three classification models: Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and AdaBoost using both structured and unstructured information from the patient's clinical records. Results show that the best classification model is MLP using only unstructured data, obtaining 89% of precision, showing the usefulness of this type of data in the classification of cancer patients.

scholarly journals Sentiment Analysis towards Actionable Intelligence via Deep Learning

TEM Journal ◽  
2020 ◽  
pp. 1663-1668
Author(s):  
Shorouq Fathi Eletter
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Service Providers ◽  
Unstructured Data ◽  
Classification Model ◽  
Online Review ◽  
Support Vector ◽  
Competitive Advantages ◽  
Accuracy Rate ◽  
Vector Machines

The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.

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 Classification of hyperspectral endocrine tissue images using support vector machines

2020 ◽  
Vol 16 (5) ◽  
pp. 1-10
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Magarita Ivanova ◽  
Thomas Neumuth ◽  
Nada Rayes ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Endocrine Tissue ◽  
Vector Machines

scholarly journals Support vector machines for classification of input vectors with different metrics

2011 ◽  
Vol 61 (9) ◽  
pp. 2874-2878 ◽  
Author(s):  
L. Gonzalez-Abril ◽  
F. Velasco ◽  
J.A. Ortega ◽  
L. Franco
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

A Hybrid Methodology For On-Line Process Monitoring

2004 ◽  
Vol 2 (1) ◽  
Author(s):  
Rakesh Kumar ◽  
Avinash M. Jade ◽  
Valadi K. Jayaraman ◽  
Bhaskar D. Kulkarni
Keyword(s):  
Process Monitoring ◽  
Current Practice ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Nonlinear Dimensionality Reduction ◽  
Hybrid Strategy ◽  
Vector Machines ◽  
On Line ◽  
Line Process

A hybrid strategy of using (i) locally linear embedding for nonlinear dimensionality reduction of high dimensional data and (ii) support vector machines for classification of the resultant features is proposed as a robust methodology for process monitoring. Illustrative examples substantiate the methodology vis-à-vis current practice.

scholarly journals Detection and Classification of Organophosphate Nerve Agent Simulants Using Support Vector Machines with Multiarray Sensors

2004 ◽  
Vol 44 (2) ◽  
pp. 499-507 ◽  
Author(s):  
Omowunmi Sadik ◽  
Walker H. Land, ◽  
Adam K. Wanekaya ◽  
Michiko Uematsu ◽  
Mark J. Embrechts ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Nerve Agent ◽  
Support Vector ◽  
Vector Machines
Sign in / Sign up

Export Citation Format

Share Document