The Research of Support Vector Machine in Agricultural Data Classification

Transcription

1 The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou Chna Zhengzhou Commodty Exchange, Zhengzhou Chna slecn@6.com, dqgcn@6.com, xnmngma@6.com, wengm@63.com Abstract. The agrcultural data classfcaton s a hot topc n the feld of precson agrculture. Support vector machne (SVM) s a knd of structural rsk mnmzaton based learnng algorthms. As a popular machne learnng algorthm, SVM has been wdely used n many felds such as nformaton retreval and text classfcaton n the last decade. In ths paper, SVM s ntroduced to classfy the agrcultural data. An expermental evaluaton of dfferent methods s carred out on the publc agrcultural dataset. Expermental results show that the SVM algorthm outperforms two popular algorthms,.e., nave bayes and artfcal neural network n terms of the F measure. Keywords: Support Vector Machne, Agrcultural Data, Classfcaton Introducton As a very promsng feld wth a huge growth potental, agrcultural data classfcaton s a hot topc n the agrculture and computer scence communtes. In recent years, many popular algorthms n the machne learnng feld have been appled n the agrcultural data classfcaton, such as decson tree [], knn [], artfcal neural network [3, 4], etc. Support vector machne (SVM) [5, 6], ntroduced by Vapnk and Chervonenks n 97, s a machne learnng algorthm based on statstcal learnng theory. By usng nonlnear kernel functons, SVM can map orgnal nput data nto a hgh dmensonal feature space to seek a separate hyperplane, and then t can perform classfcaton by usng the constructed N-dmensonal hyperplane that optmally separates the data nto two categores. For the past few years, SVM has been wdely used n dfferent felds and t can obtan hgh performance n many real world classfcaton applcatons such as mage retreval [7], cancer recognton [8], text classfcaton [9, 0] and credt scorng [-3]. In ths paper, SVM s ntroduced to classfy the agrcultural data. Experments on real agrcultural dataset have been conducted and the expermental results ndcate that the SVM algorthm outperforms two popular algorthms,.e., nave bayes and artfcal neural network n terms of the F measure. Thus, SVM s an effectve method for agrcultural data classfcaton.

2 The remander of the paper s organzed as follows: Secton gves an ntroducton of SVM n detal. Secton 3 reports and dscusses the expermental results and fnally Secton 4 states the conclusons of our work. SVM As a popular machne learnng algorthm, SVM s a new generaton learnng system based on recent advances n statstcal learnng theory. It realzes the theory of VC dmenson and prncple of structural rsk mnmum to consttute an objectve functon and then fnd a partton hyperplane that can satsfy the class requrement. The basc dea of SVM can be descrbed as follows. Frstly, search an optmal hyperplane satsfes the request of classfcaton. Secondly, use a certan algorthm to make the margn of the separaton besde the optmal hyperplane maxmum whle ensurng the accuracy of correct classfcaton. Then, the separable data can be classfed nto classes effectvely [6]. As a knd of structural rsk mnmzaton based learnng algorthms, SVM have better generalzaton abltes comparng to other tradtonal emprcal rsk mnmzaton based learnng algorthms. An llustraton of the SVM s shown n Fg.. Maxmal margn hyperplane Support Vectors Fg.. An llustraton of SVM In a SVM classfer, let the tranng set be {(x, y ), (x, y ),, (x n, y n )}, where x s an nput vector and y ts label. The partton hyperplane can be defned as [6] xb 0. () where b s the offset of hyperplane; s the normal vector of the partton hyperplane. A partton hyperplane to make the blateral blank area,.e., /, maxmum must be found to make the partton hyperplane as far from the pont n tranng dataset as possble, whch can be defned as follows. Mnmze ( )=. A constrant condton must be met, whch s defned as follows. () y ( x b ). (3)

3 The lagrange functon can be defned as: L(, b, ) ( ) ( y ( x b ) ). n (4) Subject to the followng two condtons,.e., n y 0 and 0, then the followng formula can be defned for seekng the mnmum of lagrange functon. n n max Q( ) y y ( x x ). j j j, j The optmal class functon can be defned as follows. * * * ( ) sgn(( ) ) sgn( n ( ) * ) f x x b y x x b. An mportant advantage of SVM s that t can be analyzed theoretcally usng concepts from computatonal learnng theory, and obtan state-of-the-art performance. Recently, t has also been appled to a number of real-world problems such as handwrtten characters recognton, nformaton retreval and the classfcaton of bomedcal data. In ths paper, SVM s ntroduced to classfy the agrcultural data for mprovng the classfcaton performance of agrcultural data. (5) (6) 3 Expermental Results To study the effectveness of the SVM method for agrcultural classfcaton, we test t on agrcultural data n ths secton. One agrcultural dataset obtaned from agrcultural researchers n New Zealand,.e., the whte-clover dataset [4], s used n experment. The objectve of the whte-clover dataset s to determne the mechansms whch nfluence the persstence of whte clover populatons n summer dry hll land. We used the F measure to evaluate the performance of algorthm. A confuson matrx contans nformaton about actual and predcted classfcatons done by a classfcaton system. The table shows confuson matrx for two class classfer [5]. Table. Cases of the classfcaton for one class Class C Belong Result of classfer Not belong Real classfcaton Belong TP FN Not belong FP TN Several standard terms can be defned for the two class matrx. The recall s the proporton of postve patterns that were correctly dentfed, as calculated usng the equaton:

4 TP recall TP FN. (7) Precson s the proporton of the predcted postve patterns that were correct, as calculated usng the equaton: precson TP. TP FP Then, the performance of the classfcaton can be evaluated n terms of F measure. F precson recall precson recall For SVM, we used the LIBSVM [6] for SVM mplementaton and set lnear functon as default kernel functon of SVM. To evaluate the effectveness of SVM n agrcultural data classfcaton, two popular algorthms,.e., nave bayes [7] and artfcal neural network [8], are mplemented and used as benchmarks for comparson. Performance s evaluated by 0-fold cross valdaton. Fg. shows the classfcaton results of SVM, nave bayes and artfcal neural network n terms of F measure on the dataset. The F value of SVM s 67.3%, whch s approxmately 6.9% hgher than that of nave bayes algorthm and 4.8% hgher than that of artfcal neural network algorthm. (8) (9) 0.7 nave bayes ANN SVM Fg.. Comparson of the F of classfcaton on dataset 4 Concluson The classfcaton of agrcultural data s an mportant applcaton of nformaton technology n agrculture. SVM s a powerful state-of-the-art classfer and has been appled n many felds. In ths paper, SVM s ntroduced to classfy the agrcultural data for mprovng the classfcaton performance. The expermental results show that the SVM s an effectve method for classfcaton of agrcultural data.

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