Total views : 252
Supervised SVM Classification of Rainfall Datasets
Objectives: The model built in this paper is used to classify the rainfall datasets in identifying districts of more rainfall and of lesser rainfall in the state of Andhra Pradesh. Methods: In this paper support vector machine, random forest, Knearest neighbor and decision tree classification methods have been used to classify rainfall data sets which is divided into training set and test set for classification and later validation of the obtained results. Findings: Based on various statistical parameters like sensitivity, prevalence, detection rate, specificity, and detection prevalence it has been concluded that support vector machine classification methods is better than any other classification method used in the research. Rainfall data sets are used to initially build the classification model and the results are tested against the test set. Using the confusion matrix thus obtained the mentioned statistical parameters are obtained to establish the supremacy of support vector machine classification method. Applications: Examples of satellite imagery has become ever more significant in numerous application domains such as ecology monitoring and alternative discovery. Rainfall classification is the application used herein.
Classification, Data Mining, Classifier, Support Vector Machines, SVM.
- Poongothai K, Mathivanan M, Senthilkumaran T, Revathi S. Integrated cluster-based rule induction mining of temporal data for time-series analysis. Indian Journal of Science and Technology. 2016 Dec; 9(47):1–8. Crossref
- Malvoni M, De Giorgi MG, Congedo PM. Data on Support Vector Machines (SVM) model to forecast photovoltaic power. Data in Brief. 2016 Dec; 9:13–6. PMid:27622206 PMCid:PMC5008053. Crossref
- Verma A, Kaur I, Kaur A. Algorithmic approach to data mining and classification techniques. Indian Journal of Science and Technology. 2016 Jul; 9(28):1–22. Crossref
- Yin S, Yin J. Tuning kernel parameters for SVM based on expected square distance ratio. Information Science. 2016 Nov; 370(371):92–102. Crossref
- Kumari AD, Vineela Y, Krishna MT, Kumar SB. Analyzing and performing privacy preserving data mining on medical databases. Indian Journal of Science and Technology. 2016 May; 9(17):1–9.
- Chikalov I, Hussain S, Moshkov M. Totally optimal decision trees for Boolean functions. Discrete Applied Mathematics.2016 Dec 31; 215:1–13. Crossref
- Shenglei P, Qinghua H, Chen C. Multivariate decision trees with monotonicity constraints. Knowledge-Based Systems.2016 Nov 15; 112:14–25. Crossref
- Kim K. A hybrid classification algorithm by subspace partitioning through semi-supervised decision tree. Pattern Recognition. 2016 Dec; 60:157–63. Crossref
- Kozak J, Boryczka U. Collective data mining in the ant colony decision tree approach. Information Sciences. 2016 Dec 1; 372:126–47. Crossref
- Meher PK, Sahu TK, Rao AR. Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier. Gene. 2016 Nov 5; 592:316–24.PMid: 27393648. Crossref
- Tanaka K, Kinkyo T, Hamori S. Random forests-based early warning system for bank failures. Economics Letters. 2016 Nov; 148:118–21. Crossref
- Vogels MFA, Jong SMD, Sterk G, Addink EA. Agricultural cropland mapping using black-and-white aerial photography.Object-Based Image Analysis and Random Forests.International Journal of Applied Earth Observation and Geoinformation. 2017 Feb; 54:114–23. Crossref
- Béjaoui B, Armi Z, Ottaviani E, Barelli E, Gargouri-Ellouz E, Chérif R, Turki S, Solidoro C, Aleya L. Random Forest model and TRIX used in combination to assess and diagnose the trophic status of Bizerte Lagoon, southern Mediterranean. Ecological Indicators. 2016 Dec; 71:293– 301. Crossref
- Chelgani CS, Matin SS, Makaremi S. Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method.Measurement. 2016 Dec; 94:416–22. Crossref
- Yan R, Ma Z, Zhao Y, Kokogiannakis G. A decision tree based data-driven diagnostic strategy for air handling units.Energy and Buildings. 2016 Dec 1; 133:37–45. Crossref
- Deng Z, Zhu X, Cheng D, Zong M, Zhang S. Efficient KNN classification algorithm for big data. Neuro Computing.2016 Jun 26; 195:143–8. Crossref
- Wang Y, Chaib-Draa B. KNN-based kalman filter: An efficient and non-stationary method for gaussian process regression. Knowledge-Based Systems. 2016 Dec 15; 114:148–55. Crossref
- Gutiérrez DP, Lastra M, Bacardit J, Benítez MJ, Herrera F.GPU-SME-kNN: Scalable and memory efficient kNN and lazy learning using GPUs. Information Sciences. 2016 Dec 10; 373:165–82. Crossref
- Dong-wei X, Wang Y, Jia L, Hai-jian L, Zhang G. Real-time road traffic states measurement based on Kernel-KNN matching of regional traffic attractors. Measurement. 2016 Dec; 94:862–72. Crossref
- Tang Y, Jing L, Hui L, Atkinson PM. A multiple-point spatially weighted k-NN method for object-based classification.International Journal of Applied Earth Observation and Geoinformation. 2016 Oct; 52:263–74. Crossref
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.