Application of Machine Learning Tools for Predicting Determinant Factors
Main Article Content
Abstract
Abstract: Machine learning is a technique of optimizing a performance criterion using example data and past experience. Data in machine learning plays a key role, and machine leaning tools are used to discover and learn knowledge from the datasets stored.
The purpose of this research is to build a model that can predict the determinant factors for crop production status using machine learning techniques as a means of visualizing the data. In order to conduct this research supervised machine learning techniques were employed. For the purpose of this research, the datasets were collected from selected region agricultural offices.
The data sets used for the training and testing of the predictive model is 10,000 instances with 41 regular attributes. As a result, for identifying the determinant factors Rapid Miner machine learning tool was used. In order to find the best predictive modeling technique different experiments were conducted using Random Forest, Decision tree, Naïve Bays and ID3 predictive models. To validate the predictive performance of the selected models split and cross validation testing methods was used.
As the findings of this research shows that, Random Forest and decision tree models were performed the highest accuracy and precision than others. Therefore, the Random Forest predictive modeling have been used to predict the determinate factors form small and large datasets.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
E. Alpaydin, Introduction to machine learning, 2nd ed., The MIT Press, 2010.
Shangran Li 2019 J. Phys.: Conf. Ser. 1168 032132
Shai Shalev-Shwartz and Shai Ben-David (2014).Understanding Machine Learning from Theory to Algorithms.
Taiwo, O. A. (2010). Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), ISBN: 978-953-307-034-6, InTech, University of Portsmouth United Kingdom. Pp 3 – 31.
A. Seyoum, P. Dorosh and S. Asrat, "Crop Production in Ethiopia: Regional Patterns and Trends", Ethiopian development research institute, 2011. [Online]. Available: http://reliefweb.int/sites/reliefweb.int/files/resources/essprn11 pdf. [Accessed: 09- Jan 2016].
Supervised Machine Learning Algorithms: Classification and Comparisonhttps://www.researchgate.net/publication/318338750
Cheng, J., Greiner, R., Kelly, J., Bell, D.& Liu, W. (2002). Learning Bayesian networks from data: An information theory based approach. Artificial Intelligence Volume 137, pp. 43 – 90.
Good, I.J. (1951). Probability and the Weighing of Evidence, Philosophy Volume 26, Issue 97, 1951. Published by Charles Griffin and Company, London 1950.Copyright © The Royal Institute of Philosophy 1951, pp. 163-164.doi: https://doi.org/10.1017/S0031819100026863.
Domingos, P. & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning Volume 29, pp. 103–130 Copyright © 1997 Kluwer Academic Publishers. Manufactured in The Netherlands.
Hormozi, H., Hormozi, E. & Nohooji, H. R. (2012). The Classification of the Applicable Machine Learning Methods in Robot Manipulators. International Journal of Machine Learning and Computing (IJMLC), Vol. 2, No. 5, 2012 doi: 10.7763/IJMLC.2012.V2.189pp. 560 – 563.
Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica 31 (2007). Pp. 249 – 268.
T. Hastie, R. Tibshirani, J. H. Friedman (2001) ― The elements of statistical learning,‖ Data mining, inference, and prediction, 2001, New York: Springer Verlag.
Setiono R. and Loew, W. K. (2000), FERNN: An algorithm for fast extraction of rules from neural networks, Applied Intelligence.
Witten, I. H. & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques (2nd ed.), ISBN: 0-12-088407-0, Morgan Kaufmann Publishers, San Francisco, CA, U.S.A. © 2005 Elsevier Inc.
Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Clarendon Press, Oxford, England. 1995. Oxford University Press, Inc. New York, NY, USA ©1995.
Neocleous C. & Schizas C. (2002). Artificial Neural Network Learning: A Comparative Review. In: Vlahavas I.P., Spyropoulos C.D. (eds) Methods and Applications of Artificial Intelligence. Hellenic Conference on Artificial IntelligenceSETN 2002. Lecture Notes in Computer Science, Volume 2308. Springer, Berlin, Heidelberg, doi: 10.1007/3-540-46014-4_27 pp. 300-313.
Chekole, Assefa & Beshah, Tibebe. (2019). Application of Data Mining Tools for Identifying Determinant Factors for Crop Productivity. International Journal of Computer Applications. 181. 16-21. 10.5120/ijca2019918497.