Classification of maize genotypes by artificial neural network-based method: self organizing feature map
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Authors
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SUKANTA DASH
Indian Agricultural Statistics Research Institute, New Delhi 110 025
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S D WAHI
Indian Agricultural Statistics Research Institute, New Delhi 110 025
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A R RAO
Indian Agricultural Statistics Research Institute, New Delhi 110 025
Keywords:
ANN, City-block distance, Cluster analysis, Euclidean distance, MaizeAbstract
Seventy seven maize (Zea mays L.) genotypes collected from Annual progress report 2005-06 of All India Coordinated Maize Improvement Project, Directorate of Maize Research are classified by 6 different clustering methods including ANN and compared based on probability of misclassification. The percentage probability of misclassification for small, moderate and large sample sizes based on ANN method was 5.666, 5.417 and 4.534 respectively. The second best method for small sample size was Ward’s method with 9.333 as percentage probability of misclassification. Whereas for moderate and large sample sizes K-means method was the second best method with 6.984 and 6.899 as percentage probability of misclassification. Hence, it can be concluded that the performance of ANN method is the best among the six methods of clustering irrespective of the sample size and dissimilarity measures used.
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References
Johnson R A and Wichern D W. 2006. Applied Multivariate Statistical Analysis. 5th edn., London, Inc. Pearson Prentice Hall.
Kohonen T. 1988. An introduction to neural computing. Neural Networks, 1: 3–16.
Tudu B, Bhattacharya N, Jana A, Ghosh D and Bandyopadhyay R. 2007. Self-organizing map based classification of smell stages of black tea Fermentation process using flectronic nose. 3rd Indian International Conference on Artificial Intelligence, 1626– 35.
Zupan J. 1994. Introduction to Artificial Neural Network (ANN) Methods. Acta Chemical Slovenica 41 (3): 327–52.
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