Research of Text Classification Technology based on Genetic Annealing Algorithm

Zhu, Zhenfang; Liu, Peiyu; Lu, Ran

doi:10.1109/iscid.2008.83

Cited by 5 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now several researchers used this method for the improvement of the text classification process. In authors in [82] introduced the genetic algorithm to text categorization and used to build and optimize the user template, and also introduced simulated annealing to improve the shortcomings of ge-netic algorithm. In the experimental analysis, they show that the improved method is feasible and effective for text classification.…”

Section: H Genetic Algorithmmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Text-Documents Classification

Baharudin¹,

Lee²,

Khan³

2010

JAIT

283

109

View full text Add to dashboard Cite

With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and knowledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing techniques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing literature.

show abstract

Section: H Genetic Algorithmmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Text-Documents Classification

Baharudin¹,

Lee²,

Khan³

2010

JAIT

283

109

View full text Add to dashboard Cite

show abstract

“…A concentrate by Zhen-fang, Pei-yu, and Ran (2008) classified news stories considering geological data, as well as fleeting articulations related with them. Word vector and word scattering parallelly communicated the proposed model for this review.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This calculation has been utilized by a few scientists to further develop the text classification process. Zhen-fang et al (2008) involved genetic calculation in text order interestingly. They fabricated and advanced the client format.…”

Section: Literature Reviewmentioning

confidence: 99%

News Location Classification

Ahmad¹,

Bhatti²

2021

iRASD J. Comp. & Info Tech.

View full text Add to dashboard Cite

Every day there are a lot of things that happen around the world. There are various ways to record every event that is occurring around the world, such as news, blogs, and articles. Over the past few years, there are multiple news available on every event that has occurred. It adds to the size of information that is available for human beings to consume. People are, moving from paper-based newspapers to digital newspapers to get their daily feed of news and digitization has a role to play in this behaviour. These days every person is preoccupied with a lot of work, online and offline, as mentioned earlier the amount of information is being increased with every passing day. For this reason, people are only interested in news that match their interests. A large amount of data in the form of text is available online, hence its classification based on its hidden features can lead to the better recommendation of news to individuals. In this research work, we have used focus area and temporal features to classify news using a Convolutional Neural Network (CNN). The results of the proposed methodology in the form of precision, accuracy, recall, and F1-Score show that these features indeed can be used for recommender systems.

show abstract

“…Now several researchers used this method for the improvement of the text classification process. In [17] authors introduced the genetic algorithm for text categorization and used to build and optimize the user template, and also introduced simulated annealing to improve the shortcomings of genetic algorithm. In the experimental analysis, they show that the improved method is feasible and effective for text classification.…”

Section: Decision Rules Classificationmentioning

confidence: 99%

Survey of Machine Learning Techniques in Textual Document Classification

Mohod¹,

Dhote²

2014

IOSRJCE

View full text Add to dashboard Cite

Classification of Text Document points towards associating one or more predefined categories based on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays an important role in training the system with predefined categories. The importance of Machine learning approach has felt because of which the study has been taken up for text document classification based on the statistical event models available. The aim of this paper is to present the important techniques and methodologies that are employed for text documents classification, at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques.

show abstract

Research of Text Classification Technology based on Genetic Annealing Algorithm

Cited by 5 publications

References 3 publications

A Review of Machine Learning Algorithms for Text-Documents Classification

A Review of Machine Learning Algorithms for Text-Documents Classification

News Location Classification

Survey of Machine Learning Techniques in Textual Document Classification

Contact Info

Product

Resources

About