An Automatic Document Classifier System Based on Genetic Algorithm and Taxonomy

Diaz-Manríquez, Alan; Ríos-Alvarado, Ana B.; Barrón-Zambrano, José Hugo; Guerrero-Melendez, Tania Yukary; Elizondo-Leal, Juan Carlos

doi:10.1109/access.2018.2815992

Cited by 13 publications

(12 citation statements)

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“…The template webpage is represented by the variable mwp. Next, we use MFPSDDP and Formula (1) to calculate similarity between a webpage in TWPS ∪ NTWPS and mwp. When using MFPSDDP, the same relationships RPSA and CSR are used respectively to calculate the similarity.…”

Section: Selection Of Same Relationship Between Two Nodesmentioning

confidence: 99%

“…Step 4: The filtering thread filters the 100 webpages once. The filtering thread uses Algorithm 1 and Formula (1) to calculate the similarity between the webpage to be filtered and the template webpage. The filtering thread determines whether the webpage to be filtered belongs to the type of target webpages according to the similarity threshold in the software configuration.…”

Section: Double Thread Designmentioning

confidence: 99%

“…This condition is expressed as follows: page.onlyContainTags (divTagAndspanTag) Condition 2. The similarity between each webpage and a template webpage is calculated by CSR and Formula (1). The similarity threshold is set to 0.2.…”

Section: Mfpsddpmentioning

confidence: 99%

“…Filtering methods need to be proposed and developed. Some existing studies have proposed many methods to filter webpages [1,2]. Among the existing filtering methods, some are based on structure [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Some existing studies have proposed many methods to filter webpages [1,2]. Among the existing filtering methods, some are based on structure [1,2]. If programmers use filtering methods based on structure, then programmers should know part of structures of webpages in advance.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Filtering Pages by Similarity Degree based on Dynamic Programming

Deng

2018

Future Internet

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To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd.com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS).

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Section: Selection Of Same Relationship Between Two Nodesmentioning

confidence: 99%

Section: Double Thread Designmentioning

confidence: 99%

Section: Mfpsddpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method for Filtering Pages by Similarity Degree based on Dynamic Programming

Deng

2018

Future Internet

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A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,700

761

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In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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