A novel attribute weighting method with genetic algorithm for document classification

2016 ◽  
Author(s):  
Sinan Ay ◽  
Yavuz Selim Dogan ◽  
Seyfullah Alver ◽  
Cetin Kaya
Keyword(s):  
Genetic Algorithm ◽  
Document Classification ◽  
Weighting Method ◽  
Attribute Weighting

8 Genetic algorithm and normalized text feature based document classification

2021 ◽  
pp. 123-136
Author(s):  
Vishal Sahu ◽  
Amit Kumar Mishra ◽  
Vivek Sharma ◽  
Ramakant Bhardwaj
Keyword(s):  
Genetic Algorithm ◽  
Document Classification ◽  
Text Feature ◽  
Feature Based

Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 314-317
Author(s):  
Di Wu ◽  
Sheng Yao Yang ◽  
J.C. Liu
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
Performance Optimization ◽  
Optimization Problem ◽  
Simulation Analysis ◽  
Multi Objective Optimization ◽  
Weighting Method ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Decision Engine

The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time requirement.

Computer aided diagnostic system based on SVM and K harmonic mean based attribute weighting method

2020 ◽  
Vol 19 ◽  
pp. 100270 ◽  
Author(s):  
Anand Kumar Srivastava ◽  
Yugal Kumar ◽  
Pradeep Kumar Singh
Keyword(s):  
Diagnostic System ◽  
Harmonic Mean ◽  
Weighting Method ◽  
Attribute Weighting ◽  
Computer Aided

Efficient Kernel Extreme Learning Machine and Neutrosophic C-means-based Attribute Weighting Method for Medical Data Classification

2020 ◽  
Vol 29 (16) ◽  
pp. 2050260 ◽  
Author(s):  
D. Shiny Irene ◽  
T. Sethukarasi
Keyword(s):  
Extreme Learning Machine ◽  
Data Classification ◽  
Medical Data ◽  
Integrated System ◽  
Weighting Method ◽  
Attribute Weighting ◽  
Kernel Extreme Learning Machine ◽  
Medical Data Classification ◽  
Learning Machine

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.

scholarly journals Regionalization of potential evapotranspiration using a modified region of influence

2019 ◽  
Vol 140 (1-2) ◽  
pp. 115-127 ◽  
Author(s):  
Marzieh Hasanzadeh Saray ◽  
Seyed Saeid Eslamian ◽  
Björn Klöve ◽  
Alireza Gohari
Keyword(s):  
Shannon Entropy ◽  
Potential Evapotranspiration ◽  
Urmia Lake ◽  
Lake Basin ◽  
Weighting Method ◽  
Attribute Weighting ◽  
Skewness Coefficient ◽  
Weather Stations ◽  
Homogeneous Regions ◽  
Urmia Lake Basin

AbstractThis study examined the effect of different attributes on regionalization of potential evapotranspiration (ETp) in Urmia Lake Basin (ULB), Iran, using the region of influence (RoI) framework. Data for the period 1997–2016 from 30 weather stations were selected for the analysis. To achieve similarity between stations, climate, geographical, and statistical attributes were selected. To determine the effect of each attribute, the Shannon entropy weighting method was used. The results showed that attribute weighting had a significant impact on ETp clustering. Among the groups studied, the most significant effect of weighting was observed in the statistical attributes category. Among all attributes, skewness coefficient (CS) was the most useful in determining similarity between stations. Based on the results, ULB can be divided into three homogeneous regions. Proximity of weather stations did not always indicate similarity between them, but by weighting the stations in addition to weighting the attributes, more accurate estimates of ETp in the basin were obtained. Overall, the results demonstrate potential for application of the RoI approach in regionalization of ETp, by assigning a weight to weather stations and to influencing attributes.

A Novel Approach for Ontology-Based Dimensionality Reduction for Web Text Document Classification

2017 ◽  
Vol 5 (4) ◽  
pp. 44-58 ◽  
Author(s):  
Mohamed K. Elhadad ◽  
Khaled M. Badran ◽  
Gouda I. Salama
Keyword(s):  
Dimensionality Reduction ◽  
Feature Vector ◽  
Text Processing ◽  
Principal Component ◽  
Vital Role ◽  
Document Classification ◽  
Weighting Method ◽  
Text Documents ◽  
Text Document ◽  
Text Document Classification

Dimensionality reduction of feature vector size plays a vital role in enhancing the text processing capabilities; it aims in reducing the size of the feature vector used in the mining tasks (classification, clustering, etc.). This paper proposes an efficient approach to be used in reducing the size of the feature vector for web text document classification process. This approach is based on using WordNet ontology, utilizing the benefit of its hierarchal structure, to eliminate words from the generated feature vector that has no relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting method. The proposed ontology based approach was evaluated against the Principal component analysis (PCA) approach using several experiments. The experimental results reveal the effectiveness of the authors' proposed approach against other traditional approaches to achieve a better classification accuracy F-measure, precision, and recall.

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