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Improved Classification of Incomplete Pattern Using Hierarchical Clustering
Authors(6) :-Shivani A. Kurekar, Payal D. Nagpure, Kajal Kartar, Mayuri J. Patil, Priyanka Waghdhare, Prof. Vishesh P. Gaikwad
More often than not esteems are missing in database, which ought to be managed. Missing characteristics are occurred in light of the way that, the data area individual did not know the right regard or frustration of sensors or leave the space cleanse. The game plan of missing regarded inadequate case is a trying errand in machine learning approach. Divided data isn't proper for classification handle. Right when lacking illustrations are masterminded using prototype esteems, the last class for comparative cases may have distinctive results that are variable yields. We can't portray specific class for specific illustrations. The framework makes a wrong result which also realizes varying effects. So to oversee such kind of deficient data, framework executes prototype-based credal classification (PCC) method. The PCC method is melded with Hierarchical batching and evidential thinking methodology to give correct, time and memory gainful outcomes. This procedure readies the examples and perceives the class prototype. This will be useful for distinguishing the missing characteristics. By then in the wake of getting each and every missing worth, credal procedure is use for classification. The trial comes to fruition show that the enhanced type of PCC performs better similar to time and memory viability.
Shivani A. Kurekar, Payal D. Nagpure, Kajal Kartar, Mayuri J. Patil, Priyanka Waghdhare, Prof. Vishesh P. Gaikwad
Belief functions, hierarchical clustering, credal classification, evidential reasoning, missing data.
- Zhun-Ga Liu, Quan Pan, Grgoire Mercier, and Jean Dezert, “A New Incomplete Pattern Classication Method Based on Evidential Reasoning”, North-westernPolytechnical University, Xian 710072, China,4, APRIL 2015
- Pedro J. Gracia-Laencina, Jose-Luis Sancho-Gomez, Pattern classification with missing data: a review, Universidad Politecnica de Cartagena, Dpto. Tecnologias de la Informacion y lasCommunicaciones, Plaza del Hospital 1, 30202, Cartagena (Murcia), Spain, 2010.
- Satish Gajawada and Durga Toshniwal, “Missing Value Imputation Method Based on Clustering and Nearest Neighbours”, The Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, India, 2012.
- Cristobal J. Carmona, Julian Luengo, “An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery”, Department of Computer Science, University of Jaen, Campus lasLagunillas, 23071 Jaen, Spain, 2012.
- K.Pelckmans,J.D.Brabanter, J. A. K. Suykens,and B.D.Moor,“Handling missing values in support vector machine classifiers, Neural Netw., vol. 18, nos. 5-6, pp. 684-692, 2005.
- P. Chan and O. J. Dunn, “The treatment of missing values in discriminant analysis,” J. Amer. Statist. Assoc., vol. 6, no. 338, pp. 473477, 1972.
- F. Smarandache and J. Dezert, “Information fusion based on new proportionalconflict redistribution rules,” in Proc. Fusion Int. Conf. Inform.Fusion, Philadelphia, PA, USA, Jul. 2005.
- J. L. Schafer, Analysis of Incomplete Multivariate Data. London, U.K.: Chapman Hall, 1997.
- O. Troyanskaya et al., “Missing value estimation method for DNA microarrays,” Bioinformatics, vol. 17, no. 6, pp. 520525, 2001.
- G. Batista and M. C. Monard, “A study of K-nearest neighbour as an imputation method,” in Proc. 2nd Int. Conf. Hybrid Intell. Syst., 2002, pp. 251260.
- Farhangfar, Alireza, Lukasz Kurgan, “Impact of imputation of missing values on classification error for discrete data”, Pattern Recognition, pp. 3692-3705, 2008.
- F. Smarandache and J. Dezert, “On the consistency of PCR6 with the averaging rule and its application to probability estimation”, Proceedings of the International Conference on Information Fusion, pp.323-330, July 2013.
- Z.-G. Liu, J. Dezert, G. Mercier, and Q. Pan, “Belief C-means: An extension of fuzzy C-means algorithm in belief functions framework,” Pattern Recognition, vol. 33, no. 3, pp. 291–300, 2012.
- P. Garcia-Laencina, J. Sancho-Gomez, A. Figueiras-Vidal, “Pattern classi?cation with missing data: A review”, Neural Networks, vol. 19, no. 2, pp. 263–282, 2010.
- A. Tchamova, J. Dezert, “On the Behavior of Dempster’s rule of combination and the foundations of Dempster–Shafer theory”, In proceedings of Sixth IEEE International Conference on Intelligent Systems, pp. 108–113, 2012.
Published in : Volume 4 | Issue 7 | March-April 2018
Date of Publication : 2018-04-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 93-99
Manuscript Number : IJSRST11845230
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Shivani A. Kurekar, Payal D. Nagpure, Kajal Kartar, Mayuri J. Patil, Priyanka Waghdhare, Prof. Vishesh P. Gaikwad, "Improved Classification of Incomplete Pattern Using Hierarchical Clustering ", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 7, pp.93-99, March-April-2018
URL : http://ijsrst.com/IJSRST11845230