Abstract
Content classification is the errand of naturally arranging a lot of records into classifications from a predefined set. This implies that it allocates predefined classifications to free-content archives. This paper introduces a unique two-phase determination strategy for content classification using data gain (CCDG) that will guide and examine the hereditary calculation of the given dataset. In the first phase of CCDG, each term inside the archive is positioned depending on its significance for grouping and data gain. In the second stage, hereditary calculation through GA and main segment investigation through PCA determines and highlights the relevant extraction of the trend of the given stream of bits in decreasing impact. In this manner, all the content that has lesser significance can be overlooked while only impactful content remains for providing details. Experiments show encouraging and better results for proposed CCDG as compared to conventional methods under all the dataset and test conditions.
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References
Ertugrul, Ö.F., Tagluk, M.E.: A novel version of k nearest neighbor: dependent nearest neighbor. Appl. Soft Comput. 55, 480–490 (2017)
Singh, A., Deep, K., Grover, P.: A novel approach to accelerate calibration process of a k-nearest neighbor classifier using GPU. J. Parallel Distrib. Comput. 104, 114–129 (2017)
Parvin, H., Alizadeh, H., Minati, B.: A modification on k-nearest neighbor classifier. Glob. J. Comput. Sci. Technol. (2010)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man. Cybern. 4, 580–585 (1985)
Faziludeen, S., Sankaran, P.: ECG beat classification using evidential K-nearest neighbours. Proc. Comput. Sci. 89, 499–505 (2016)
Song, Y., Liang, J., Lu, J., Zhao, X.: An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing 251, 26–34 (2017)
Bian W.: Fuzzy-rough nearest-neighbor classification method: an integrated framework. In: Proceedings of the IASTED internet. conference on applied informatics, pp. 160–164, Austria (2002)
Nguyen, B., Morell, C., De Baets, B.: Large-scale distance metric learning for k-nearest neighbors regression. Neurocomputing 214, 805–814 (2016)
Lin, Y., Li, J., Lin, M., Chen, J.: A new nearest neighbor classifier via fusing neighborhood information. Neurocomputing 143, 164–169 (2014)
Manocha, S., Girolami, M.A.: An empirical analysis of the probabilistic k-nearest neighbour classifier. Pattern Recogn. Lett. 28(13), 1818–1824 (2007)
Sarkar, M.: Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets Syst. 158(19), 2134–2152 (2007)
Timofte, R., Van Gool, L.: Iterative nearest neighbors. Pattern Recogn. 48(1), 60–72 (2015)
Roweis S.T., Saul L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Aharon, M., Elad, M., Bruckstein, A.: SVD: an algorithm for designing over complete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Sahu S., Saurabh P., Rai S.: An enhancement in clustering for sequential pattern mining through neural algorithm using web logs. In: International conference on computational intelligence and communication networks, pp. 758–764 (2015)
Saxena, M., Saurabh, P., Verma, B.: A new hashing scheme to overcome the problem of overloading of articles in Usenet, pp. 967–975. Springer, AISC (2012)
Mishra, B.K., Saurabh, P., Verma, B.: A novel approach to classify high dimensional datasets using supervised manifold learning, pp. 22–30. Springer, CCIS (2012)
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Rathore, M.S., Saurabh, P., Prasad, R., Mewada, P. (2020). Text Classification with K-Nearest Neighbors Algorithm Using Gain Ratio. In: Das, H., Pattnaik, P., Rautaray, S., Li, KC. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 1119. Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_3
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DOI: https://doi.org/10.1007/978-981-15-2414-1_3
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