A support vector reduction method for accelerating calculation

2007 ◽  
Author(s):  
Guo-Sheng Hu ◽  
Guang-Yong Ren ◽  
Jing-Jiang Jiang
Keyword(s):  
Reduction Method ◽  
Support Vector ◽  
Vector Reduction

A proposed scheme for sentiment analysis

Kybernetes ◽  
2018 ◽  
Vol 47 (5) ◽  
pp. 957-984 ◽  
Author(s):  
Sajjad Tofighy ◽  
Seyed Mostafa Fakhrahmad
Keyword(s):  
Sentiment Analysis ◽  
Clustering Algorithm ◽  
Reduction Method ◽  
Feature Reduction ◽  
Sentiment Classification ◽  
Support Vector ◽  
Second Phase ◽  
Reduction Algorithm ◽  
Content Type ◽  
Statistical Knowledge

Purpose This paper aims to propose a statistical and context-aware feature reduction algorithm that improves sentiment classification accuracy. Classification of reviews with different granularities in two classes of reviews with negative and positive polarities is among the objectives of sentiment analysis. One of the major issues in sentiment analysis is feature engineering while it severely affects time complexity and accuracy of sentiment classification. Design/methodology/approach In this paper, a feature reduction method is proposed that uses context-based knowledge as well as synset statistical knowledge. To do so, one-dimensional presentation proposed for SentiWordNet calculates statistical knowledge that involves polarity concentration and variation tendency for each synset. Feature reduction involves two phases. In the first phase, features that combine semantic and statistical similarity conditions are put in the same cluster. In the second phase, features are ranked and then the features which are given lower ranks are eliminated. The experiments are conducted by support vector machine (SVM), naive Bayes (NB), decision tree (DT) and k-nearest neighbors (KNN) algorithms to classify the vectors of the unigram and bigram features in two classes of positive or negative sentiments. Findings The results showed that the applied clustering algorithm reduces SentiWordNet synset to less than half which reduced the size of the feature vector by less than half. In addition, the accuracy of sentiment classification is improved by at least 1.5 per cent. Originality/value The presented feature reduction method is the first use of the synset clustering for feature reduction. In this paper features reduction algorithm, first aggregates the similar features into clusters then eliminates unsatisfactory cluster.

scholarly journals Heuristic sample reduction method for support vector data description

2016 ◽  
Vol 24 ◽  
pp. 298-312 ◽  
Author(s):  
Wenzhu SUN ◽  
Jianling QU ◽  
Yang CHEN ◽  
Yazhou DI ◽  
Feng GAO
Keyword(s):  
Reduction Method ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description

An improved vector-reduction method

1991 ◽  
Vol 40 (2) ◽  
pp. 214-217 ◽  
Author(s):  
H.J. Sips ◽  
H. Lin
Keyword(s):  
Reduction Method ◽  
Vector Reduction

scholarly journals A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates

2021 ◽  
Vol 11 (23) ◽  
pp. 11156
Author(s):  
Dieter Bender ◽  
Daniel J. Licht ◽  
C. Nataraj
Keyword(s):  
Heart Surgery ◽  
Reduction Method ◽  
Periventricular Leukomalacia ◽  
Support Vector ◽  
Svm Classifier ◽  
Algorithm Optimization ◽  
Learning Framework ◽  
Svm Model ◽  
Testing Accuracy ◽  
Dimensionality Reduction Method

This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) in neonates after heart surgery. Our prior work shows that the Support Vector Machine (SVM) classifier can be a powerful tool in predicting clinical outcomes of such complicated and uncommon diseases, even when the number of data samples is low. In the presented work, we first illustrate and discuss the shortcomings of the traditional automatic machine learning (aML) approach. Consequently, we describe our methodology for addressing these shortcomings, while utilizing the designed interactive ML (iML) algorithm. Finally, we conclude with a discussion of the developed method and the results obtained. In sum, by adding an additional (Genetic Algorithm) optimization step in the SVM learning framework, we were able to (a) reduce the dimensionality of an SVM model from 248 to 53 features, (b) increase generalization that was confirmed by a 100% accuracy assessed on an unseen testing set, and (c) improve the overall SVM model’s performance from 65% to 100% testing accuracy, utilizing the proposed iML method.

scholarly journals Automated knowledge-assisted mitosis cells detection framework in breast histopathology images

2021 ◽  
Vol 19 (2) ◽  
pp. 1720-1744
Author(s):  
Xiao Jian Tan ◽  
◽  
Nazahah Mustafa ◽  
Mohd Yusoff Mashor ◽  
Khairul Shakir Ab Rahman ◽  
...  
Keyword(s):  
False Positive ◽  
Domain Knowledge ◽  
Reduction Method ◽  
Support Vector ◽  
Svm Classifier ◽  
False Positive Reduction ◽  
Histopathology Images ◽  
Automated Knowledge ◽  
Inconsistent Objects ◽  
Nucleus Segmentation

<abstract> <p>Based on the Nottingham Histopathology Grading (NHG) system, mitosis cells detection is one of the important criteria to determine the grade of breast carcinoma. Mitosis cells detection is a challenging task due to the heterogeneous microenvironment of breast histopathology images. Recognition of complex and inconsistent objects in the medical images could be achieved by incorporating domain knowledge in the field of interest. In this study, the strategies of the histopathologist and domain knowledge approach were used to guide the development of the image processing framework for automated mitosis cells detection in breast histopathology images. The detection framework starts with color normalization and hyperchromatic nucleus segmentation. Then, a knowledge-assisted false positive reduction method is proposed to eliminate the false positive (i.e., non-mitosis cells). This stage aims to minimize the percentage of false positive and thus increase the F1-score. Next, features extraction was performed. The mitosis candidates were classified using a Support Vector Machine (SVM) classifier. For evaluation purposes, the knowledge-assisted detection framework was tested using two datasets: a custom dataset and a publicly available dataset (i.e., MITOS dataset). The proposed knowledge-assisted false positive reduction method was found promising by eliminating at least 87.1% of false positive in both the dataset producing promising results in the F1-score. Experimental results demonstrate that the knowledge-assisted detection framework can achieve promising results in F1-score (custom dataset: 89.1%; MITOS dataset: 88.9%) and outperforms the recent works.</p> </abstract>

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