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General Information
    • ISSN: 2010-3697
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Engineering & Technology Digital Library, ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET).
    • E-mail ijmo@iacsitp.com
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2014Vol.4(5): 375-382 ISSN: 2010-3697
DOI: 10.7763/IJMO.2014.V4.404

Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining

Geraldin B. Dela Cruz, Bobby D. Gerardo, and Bartolome T. Tanguilig III
Abstract—Extraction of knowledge in agricultural data is a challenging task, from discovering patterns and relationships and interpretation. In order to obtain potentially interesting patterns and relationships from this data, it is therefore essential that a methodology be developed and take advantage of the sets of existing methods and tools available for data mining and knowledge discovery in databases. Data mining is relatively a new approach in the field of agriculture. Accurate information in characterizing crops depends on climatic, geographical, biological and other factors. These are very important inputs to generate characterization and prediction models in data mining. In this study, an efficient data mining methodology based on PCA-GA is explored, presented and implemented to characterize agricultural crops. The method draws improvements to classification problems by using Principal Components Analysis (PCA) as a pre processing method and a modified Genetic Algorithm (GA) as the function optimizer. The fitness function in GA is modified accordingly using efficient distance measures. The approach is to asses, the PCA-GA hybrid data mining method, using various agricultural field data sets, generate data mining classification models and establish meaningful relationships. The experimental results show improved classification rates and generated characterization models for agricultural crops. The domain model outcome may have benefits, to agricultural researchers and farmers. These generated classification models can also be utilized and readily incorporated into a decision support system.

Index Terms—Classification, data mining, genetic algorithm, k-NN, principal component analysis.

G. B. Dela Cruz is with the Institute of Engineering, Tarlac College of Agriculture, Camiling, Tarlac, Philippines. He is also with the Technological Institute of Philippines, Cubao, Quezon City, Philippines (e-mail: delacruz.geri@gmail.com).
B. D. Gerardo is with the Administration and Finance at the West Visayas Stare University, La Paz, Iloilo City, Philippines. He is also with the Department of Information Technology at WVSU (e-mail: bgerardo@wvsu.edu.ph).
B. C. Tanguilig III is with the Academic Affairs and concurrent Dean of the College of Information end Information Technology Education at the Technological Institute of the Philippines, Quezon City, Philippine (e-mail: bttanguilig_3@yahoo.com).

[PDF]

Cite: Geraldin B. Dela Cruz, Bobby D. Gerardo, and Bartolome T. Tanguilig III, "Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining," International Journal of Modeling and Optimization vol. 4, no. 5, pp. 375-382, 2014.

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