Progressive Similarity Transductive Support Vector Machine Algorithm for Small Sample Text Classification

2013 ◽  
Vol 12 (23) ◽  
pp. 7673-7676
Author(s):  
Jianbin Ma ◽  
Ying Li
Keyword(s):  
Support Vector Machine ◽  
Text Classification ◽  
Small Sample ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Transductive Support Vector Machine

scholarly journals Improving the Accuracy of Text Classification using Stemming Method, A Case of Non-formal Indonesian Conversation

2020 ◽  
Author(s):  
Rianto Rianto ◽  
Achmad Benny Mutiara ◽  
Eri Prasetyo Wibowo ◽  
Paulus Insap Santosa
Keyword(s):  
Support Vector Machine ◽  
Information Retrieval ◽  
Text Classification ◽  
Experimental Evaluation ◽  
Hate Speech ◽  
Text Processing ◽  
High Accuracy ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Text Data

Abstract Stemming has long been used in data pre-processing in information retrieval, which aims to make affix words into root words. However, there are not many stemming methods for non-formal Indonesian text processing. The existing stemming method has high accuracy for formal Indonesian, but low for non-formal Indonesian. Thus, the stemming method which has high accuracy for non-formal Indonesian classifier model is still an open-ended challenge. This study introduces a new stemming method to solve problems in the non-formal Indonesian text data pre-processing. Furthermore, this study aims to provide comprehensive research on improving the accuracy of text classifier models by strengthening on stemming method. Using the Support Vector Machine algorithm, a text classifier model is developed, and its accuracy is checked. The experimental evaluation was done by testing 550 datasets in Indonesian using two different stemming methods. The results show that using the proposed stemming method, the text classifier model has higher accuracy than the existing methods with a score of 0.85 and 0.73, respectively. In the future, the proposed stemming method can be used to develop the Indonesian text classifier model which can be used for various purposes including text clustering, summarization, detecting hate speech, and other text processing applications.

Study on the Self-Organize Selective Fusion Support Vector Machine Algorithm

2011 ◽  
Vol 282-283 ◽  
pp. 165-168
Author(s):  
Yong Ming Cai ◽  
Qing Chang
Keyword(s):  
Support Vector Machine ◽  
Classification Performance ◽  
Small Sample ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Multiple Classifiers ◽  
Memory Overhead ◽  
Selective Fusion ◽  
Statistical Learning Method ◽  
Sample Support

As a major statistical learning method in case of small sample, Support Vector Machine Algorithm (SVM) has some disadvantages in dealing with vast amounts of data, such as the memory overhead and slow training. we use Multi-class Support Vector Machine (MSVM) with Self-Organize Selective Fusion (SOSF) to optimize the multiple classifiers selectively, which can update the classification and self-adjust its classification performance, and eliminate some redundancy and conflicts, achieve the fusion of multiple classifiers selectively, and effectively solve the shortcoming of disturbances by the sub-samples distribution in large sample, and improve the training efficiency and classification efficiency.

Fault Diagnosis for Temperature Signal of Turbine Blade Based on LS-SVM

2013 ◽  
Vol 385-386 ◽  
pp. 580-584 ◽  
Author(s):  
Li Wei Chen ◽  
Chen Dong Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Turbine Blade ◽  
Small Sample ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Operation Speed ◽  
Svm Algorithm ◽  
Temperature Signal

This document discusses the support vector machine (SVM) algorithm, then discusses least squares support vector machine (LS-SVM) algorithm, at the same time, the applications of SVM in the fault diagnosis of temperature signal of turbine blade being discussed, the least squares support vector machine algorithm being used in the research of fault diagnosis, being compared with LVQ neural network, experiments result show the operation speed of the least squares support vector machine algorithm is fast, its generalization ability is stronger, SVM can solve small sample learning problems as well as no-linear, high dimension and local minimization problems in the fault diagnosis of temperature signal of turbine blade.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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