One-Against-All Methodology for Features Selection and Classification of Internet Applications

2009 ◽  
pp. 27-38
Author(s):  
José Everardo Bessa Maia ◽  
Raimir Holanda Filho
Keyword(s):  
Features Selection ◽  
Internet Applications

scholarly journals Identification features of the minutes of interrogation and ways of authorship examination

2019 ◽  
Vol 17 (3) ◽  
pp. 97-108
Author(s):  
Elena E. Abramkina
Keyword(s):  
Current Paper ◽  
Authorship Attribution ◽  
Features Selection ◽  
New Classification ◽  
Semantic Type ◽  
The Real ◽  
Language Analysis ◽  
Authorship Analysis

Forensic authorship analysis is a frequently used technique to identify the real author of an arguable document. Often enough, under study are interrogation minutes. This kind of text is difficult for examination because of its stylistic and genre characteristics: formal phrases and structure as well as different author and compiler of the document. The above features restrict the use of some levels of language analysis. This issue, however, is poorly covered in specialist literature, with only a few articles related to it. The current paper describes the main discursive features of interrogation minutes used in authorship expertise. First we look at conventional techniques of authorship expertise and discuss their limitations. Special attention is given to the analysis of the interrogation minutes genre characteristics and their influence on the whole set of identifiers. The analysis of several conventional interrogation minutes techniques singled out two central tendencies in the authorship attribution: an identification features selection with new identifiers being added. The aim of the article is to propose a solution to the problem. Our technique is based on the methods of The Federal Ministry of the Interior, but it also takes into account genre charecteristics of the interrogation minutes. A new classification of identifiers has been developed. Additional features are offered to improve the attribution accuracy. These are clarifications, which are classified according to the semantic type of the object. In the article clarifications are divided into six types and a few subtypes and are also divided into low and high informative ones. The analysis of clarification is illustrated with the example of three different interrogation minutes. The concluding part of the article is concerned with the techniques of the interrogation minutes used in authorship expertise description, materials requirements and the steps of the analysis.

scholarly journals Impact of Feature Selection on Clustering Images ofVertebral Compression Fractures

2020 ◽  
Author(s):  
Raquel Candido ◽  
Rafael Lama ◽  
Natália Chiari ◽  
Marcello Nogueira-Barbosa ◽  
Paulo Azevedo Marques ◽  
...  
Keyword(s):  
Feature Selection ◽  
Metastatic Cancer ◽  
Vertebral Compression Fractures ◽  
Features Selection ◽  
Correct Classification ◽  
Compression Fractures ◽  
Vertebral Bodies ◽  
The Impact ◽  
Aided Diagnosis

Non-traumatic Vertebral Compression Fractures (VCFs) are generally caused by osteoporosis (benign VCFs) or metastatic cancer (malignant VCFs) and the success of the medical treatment strongly depends on a fast and correct classification of VCFs. Recently, methods for computer-aided diagnosis (CAD) based on machine learning have been proposed for classifying VCFs. In this work, we investigate the problem of clustering images of VCFs and the impact of feature selection by genetic algorithms, comparing the clustering i)with all features and ii)with feature selection through the purity results. The analysis of the clusters helps to understand the results of classifiers and difficulties of differentiating images of different classes by an expert. The results indicate that features selection improved the separability of clusters and purity. Feature selection also helps to understand which attributes are most important for analysing the images of vertebral bodies.

scholarly journals Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

2020 ◽  
Vol 31 (4) ◽  
pp. 72
Author(s):  
Hayder Adnan AlSudani ◽  
Enaas M. Hussain ◽  
Enam A. Khalil
Keyword(s):  
Multilayer Perceptron ◽  
Region Of Interest ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Features Selection ◽  
Hybrid Features ◽  
Reliable Assessment ◽  
Occurrence Matrix ◽  
Two Stages

Cancer of the breast is one of the world's most prevalent causes of death for women. Early and efficient identification is important for can care choices and reducing mortality. Mammography is the most effective early breast cancer detection process. Radiologists cannot however make a detailed and reliable assessment of mammograms due to fatigue or poor image quality. The main aim of this work is to establish a new approach to help radiologists identify anomalies and improve diagnostic precision. The proposed method has been applied through the implementation of preprocessing then segmentation of the images to get the region of interest that was used to find a texture features that were calculated based on first Order (statistical features), Gray-Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns LBP (LBP). In the features selection phase mutual information (MI) algorithm is applied to choose from the extracted features collection suitable features. Finally, Multilayer Perceptron has been applied in two stages to classify the mammography images first to normal or abnormal, and secondly, classification of abnormal images into benign or malignant images. This method was implemented and gave an accuracy of 92.91 % for the first level and 93.15% for the second level classification.

An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection

2019 ◽  
Vol 82 (4) ◽  
pp. 361-372 ◽  
Author(s):  
Hidayat Ullah ◽  
Tanzila Saba ◽  
Naveed Islam ◽  
Naveed Abbas ◽  
Amjad Rehman ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Ensemble Classification ◽  
Features Selection ◽  
Fundus Images ◽  
Color Fundus Images

An Overview of RIAs Development Tools

2018 ◽  
pp. 104-137
Author(s):  
Giner Alor-Hernández ◽  
Viviana Yarel Rosales-Morales ◽  
Luis Omar Colombo-Mendoza
Keyword(s):  
Code Generation ◽  
Application Development ◽  
Internet Applications ◽  
Model Driven ◽  
Rich Internet Applications ◽  
Rapid Application Development ◽  
Support Tools ◽  
Integrated Development Environments ◽  
Development Approach

Rich Internet Applications (RIAs) development has traditionally been addressed using framework-based development approaches (i.e., using application frameworks), which usually comprise tools such as Standard Development Kits (SDKs), class libraries, and Integrated Development Environments (IDEs). Nevertheless, another development approach that relies on Model-Driven Development (MDD) methodologies and tools has recently emerged as a result of the academic and commercial effort for alleviating the lack of development methodologies and support tools especially designed for the development of RIAs. In this chapter, a new classification of RIAs development approaches is proposed by introducing a third category: Rapid Application Development (RAD) approaches. Thereby, the chapter reviews not only IDEs for frameworks-based RIA development; it also addresses other support tools for MDD and RAD such as code generation tools. Additionally, the features, scope, and limitations of the analyzed tools are discussed by means of a series of usage scenarios addressing the RIAs implementation.

Optimization of Support Vector Machine for Classification of Spyware Using Symbiotic Organism Search for Features Selection

2021 ◽  
pp. 11-21
Author(s):  
Noah Ndakotsu Gana ◽  
Shafi’i Muhammad Abdulhamid ◽  
Sanjay Misra ◽  
Lalit Garg ◽  
Foluso Ayeni ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Features Selection ◽  
Symbiotic Organism Search

scholarly journals Natural Language Processing Based Instrument for Classification of Free Text Medical Records

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Manana Khachidze ◽  
Magda Tsintsadze ◽  
Maia Archuadze
Keyword(s):  
Language Processing ◽  
Medical Records ◽  
Nearest Neighbor ◽  
Health Management ◽  
Support Vector ◽  
Free Text ◽  
Features Selection ◽  
Second Stage ◽  
History Of

According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) andK-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful.

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