The Research of Support Vector Machine in Agricultural Data Classification

The agricultural data classification is a hot topic in the field of precision agriculture. Support vector machine (SVM) is a kind of structural risk minimization based learning algorithms. As a popular machine learning algorithm, SVM has been widely used in many fields such as information retrieval and text classification in the last decade. In this paper, SVM is introduced to classify the agricultural data. An experimental evaluation of different methods is carried out on the public agricultural dataset. Experimental results show that the SVM algorithm outperforms two popular algorithms, i.e., naive bayes and artificial neural network in terms of the F 1 measure.

[1]  Céline Rouveirol,et al.  Machine Learning: ECML-98 , 1998, Lecture Notes in Computer Science.

[2]  Young-Chan Lee,et al.  Application of support vector machines to corporate credit rating prediction , 2007, Expert Syst. Appl..

[3]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Yiming Yang,et al.  An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.

[6]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[7]  Thomas F. Schatzki,et al.  Defect detection in apples by means of x-ray imaging , 1996, Other Conferences.

[8]  Hyunsoo Kim,et al.  Dimension Reduction in Text Classification with Support Vector Machines , 2005, J. Mach. Learn. Res..

[9]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[10]  Jean-Marie Aerts,et al.  AP—Animal Production Technology: Recognition System for Pig Cough based on Probabilistic Neural Networks , 2001 .

[11]  Upmanu Lall,et al.  A k‐nearest‐neighbor simulator for daily precipitation and other weather variables , 1999 .

[12]  Joachim Krieter,et al.  The analysis of simulated sow herd datasets using decision tree technique , 2004 .

[13]  Giorgio Valentini,et al.  Cancer recognition with bagged ensembles of support vector machines , 2004, Neurocomputing.

[14]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[16]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[17]  Jonathan Crook,et al.  Support vector machines for credit scoring and discovery of significant features , 2009, Expert Syst. Appl..