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).
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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.
