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IJSTR >> Volume 8 - Issue 8, August 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



News Classification Using Hybrid Approach Of PSO-KNN

[Full Text]

 

AUTHOR(S)

Megha Singla, Brahmaleen K. Sidhu

 

KEYWORDS

SVM, KNN, PSO, Ngram

 

ABSTRACT

There are different applications which are producing the data in a big way for example various social media platforms. These data need to be analyzed and processed to extract new useful information. This information can also be useful for the decision making process for the organization. In current research there is a news related dataset. This dataset includes various types of news under different categories like technology, entertainment, political etc. various general news are to be categorized into its different categories. These categories correct entry will fine tune the whole system. Later on single sports category news are being categorized into different sports categories like cricket, rugby, football etc. Various classification techniques has been used like SVM, KNN, decision tree etc. over to it new genetic based hybrid approach has been used. This hybrid approach is PSO-KNN. It has been used for classification of the inter news classification and the intra news classification. The results have been compared on different parameters like accuracy, specificity, sensitivity etc. In all the parameters the results have shown improvement over to the SVM, KNN and Decision tree.

 

REFERENCES

• Rini Wongso, Ferdinand Ariandy Luwinda, Brandon Christian Trisnajaya, Olivia Rusli,
Rudy,” News Article Text Classification in

Indonesian Language”, ICCSCI,issue:116, pp:137-143,2017.

• Wen Zhang , Taketoshi Yoshida b , Xijin Tang c,” A comparative study of TFIDF, LSI and multi-words for text classification”, Expert

Systems with Applications,issue 38, pp:2758-2765,2011.

• Davood Mahmoodi1 , Ali Soleimani1 , Hossein
Khosravi1 , Mehdi Taghizadeh2,” FPGA

Simulation of Linear and Nonlinear Support
VectorMachine”, Journal of Software

Engineering and Applications,issue 4,pp:320-328,2011.

• Hao Lin,” Research on Energy-Efficient Text Classification”, ICITEC, 2014.

• Krina Vasa,” Text Classification through
Statistical and Machine Learning Methods: A
Survey”, IJEDR,vol. 4,issue 2, pp:655-658,2016.
• Vangelis Metsis, Ion Androutsopoulos, Georgios

Paliouras,” Spam Filtering with Naive Bayes – Which Naive Bayes?”,issue 27-28, 2006.

• C. C. Aggarwal and C. Zhai, Mining Text Data, 2012.

• M. Kepa, J. Szymanski, "Two stage SVM and kNN text documents classifier," In: Pattern

Recognition and Machine Intelligence, Kryszkiewicz M. (Ed.), Lecture Notes in Computer Science, Vol. 9124, pp. 279-289, 2015.

• R. C. Barik and B. Naik, "A Novel Extraction and Classification Technique for Machine Learning using Time Series and Statistical Approach," Computational Intelligence in Data Mining, vol. 3, pp. 217-228, 2015.

• R. Bruni and G. Bianchi, "Effective Classification Using a Small Training Set Based on Discretization and Statistical Analysis," IEEE Trans. Knowl. Data Eng., vol. 27, no. 9, pp. 2349-2361, 2015.

• Chaudhuri, "Modified fuzzy support vector machine for credit approval classification," IOS Press and Authors, vol. 27, no. 2, pp. 189-211, 2014.

• E. Baralis, L. Cagliero, and P. Garza, "EnBay: A novel pattern-based Bayesian classifier," Tkde, vol. 25, no. 12, pp. 2780- 2795, 2013.

• X. Fang, "Inference-Based Naive Bayes: Turning Naive Bayes Cost-Sensitive," vol. 25, no. 10, pp. 2302-2314, 2013.

• H. Wan, L. H. Lee, R. Rajkumar, and D. Isa, "A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine," Expert Syst. Appl., vol. 39, no. 15, pp. 11880-11888, 2012.