SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF MALIGNANT AND BENIGN LESIONS. Anatoli Nachev, Mairead Hogan

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1 311 ITHEA SUPPORT VECTOR MACHINES FOR CLASSIFICATION OF MALIGNANT AND BENIGN LESIONS Anatol Nachev, Maread Hogan Abstract: Ths paper presents an exploratory study of the effectveness of support vector machnes used as a tool for computer-aded breast cancer dagnoss. We explore the dscrmnatory power of heterogeneous mammographc and sonographc descrptors n solvng the classfcaton task. Varous feature selecton technques were tested to fnd a set of descrptors that outperforms those from smlar studes. We also explored how choce of the SVM kernel functon and model parameters affect ts predctve abltes. The kernels explored were lnear, radal bass functon, polynomal, and sgmod. The model performance was estmated by ROC analyss and metrcs, such as true and false postve rates, maxmum accuracy, area under the ROC curve, partal area under the ROC curve wth senstvty above 90%, and specfcty at 98% senstvty. Partcular attenton was pad to the latter two as lack of specfcty causes unnecessary surgcal bopses. Experments regstered that an approprate reducton of varables can greatly mprove the predctve power of the model, as long as the choce of the kernel affects the model performance margnally. We also found that the SVM s superor to the common classfcaton technque used n the feld - MLP neural networks. Keywords: data mnng, support vector machnes, heterogeneous data; breast cancer dagnoss, computer aded dagnoss. ACM Classfcaton Keywords: I.5.2- Computng Methodologes - Pattern Recognton Desgn Methodology - Classfer desgn and evaluaton. Introducton A proper treatment of breast cancer dsease requres tmely, relable, and accurate dagnoss, whch allows radologsts and physcans to dfferentate between bengn and malgnant lesons. Many computer-aded detecton/dagnoss (CAD) tools currently support medcal practces by capturng knowledge from prevous cases and applyng that knowledge to the new cases. CAD s a typcal machne-learnng problem, whch has been dealt wth by varous data mnng technques and tools such as lnear dscrmnant analyss (LDA), logstc regresson analyss (LRA), multlayer perceptons (MLP), etc. [Chen et al., 2009].

2 Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 312 Most of the current mplementatons tend to use only one nformaton source, usually mammographc data n the form of data descrptors defned by the Breast Imagng Reportng and Data System (BI-RADS) lexcon, developed by the Amercan College of Radology (ACR) n order to standardze the mammographc language and nterpretatons, and to facltate communcaton between clncans [BI-RADS, 2003], [Kopans, 1992]. Jesneck et al. [2007] have used a novel combnaton of BI-RADS mammographc and sonographc descrptors and some proposed by Stavros et al. [1995] n order to buld a predctve model based on MLP, whch shows superor characterstcs to those that use one data source. Our study takes that approach, but nvestgate another predctve technque - support vector machnes (SVM). We also address the problem of hgh false postve rate of ndcaton for bopsy (specfcty rate), whch causes unnecessary surgcal bopses, lowers the effcency of the dagnoss, exposes patents to dscomfort, and creates fnancal burden as procedures cost thousands of euros each [Lacey et al., 2002]. Further to the study of Jesneck et al. [2007] who used a set of fourteen descrptors to tran and test a MLP neural network, we explore the dscrmnatory power of all descrptors n order to seek alternatve sets that when appled to SVM can ensure even hgher senstvty and specfcty. The paper s organzed as follows: Secton 2 provdes a bref overvew of the support vector machnes used as data mnng tools; Secton 3 ntroduces the dataset used n ths study and dscusses varable selecton as part of the data preprocessng; Secton 4 presents and dscuses results obtaned from experments; and Secton 5 gves the conclusons. Support Vector Machnes Support vector machnes are common machne learnng technques. They belong to the famly of generalzed lnear models, whch acheve a classfcaton or regresson decson based on the value of the lnear combnaton of nput features. Usng hstorcal data along wth supervsed learnng algorthms, SVM generate mathematcal functons to map nput varables to desred outputs for classfcaton or regresson predcton problems. SVM, orgnally ntroduced by Vapnk [1995], provde a new approach to the problem of pattern recognton wth clear connectons to the underlyng statstcal learnng theory. They dffer radcally from comparable approaches such as neural networks because SVM tranng always fnds a global mnmum n contrast to the neural networks. SVM can be formalzed as follows. Tranng data s a set of ponts of the form, (1)

3 313 ITHEA where the c s ether 1 or -1, ndcatng the class to whch the pont x belongs. Each data pont x s a p-dmensonal real vector. Durng tranng a lnear SVM constructs a p-1- dmensonal hyperplane that separates the ponts nto two classes (see Fgure 1). Any hyperplane can be represented by: w x b = 0 where w s a normal vector and denotes dot product. Among all possble hyperplanes that mght classfy the data, SVM selects one wth maxmal dstance (margn) to the nearest data ponts (support vectors). When the classes are not lnearly separable (there s no hyperplane that can splt the two classes), a varant of SVM, called soft-margn SVM, chooses a hyperplane that splts the ponts as cleanly as possble, whle stll maxmzng the dstance to the nearest cleanly splt examples. The method ntroduces slack varables,, whch measure the degree of msclassfcaton of the datum x. Soft-margn SVM penalzes msclassfcaton errors and employs a parameter (the soft-margn constant C) to control the cost of msclassfcaton. Tranng a lnear SVM classer solves the constraned optmzaton problem (2). mn w, b, k 1 2 w s. t. 2 C n 1 w x b 1 In dual form the optmzaton problem can be represented by (3) (2) mn 1 2 n n y y x x j j j 1 j1 1 n (3) s. t. 0 C, c n 1 0 The resultng decson functon f ( x) w x b has a weght vector n w k k yk x 1 k. Data ponts x for whch 0 are called support vectors, snce they unquely defne the maxmum margn hyperplane. Maxmzng the margn allows one to mnmze bounds on generalzaton error. If every dot product s replaced by a non-lnear kernel functon, t transforms the feature space nto a hgher-dmensonal one, thus though the classfer s a hyperplane n the hgh-dmensonal feature space (see Fgure 2). The resultng classfer fts the maxmummargn hyperplane n the transformed feature space. The kernel functon can be defned as (4)

4 Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 314 where (x) maps the vector x to some other Eucldean space. The dot product x x j n the formulae above s replaced by k(x, x j ) so that the SVM optmzaton problem n ts dual form can be redefned as: maxmze (n ) 1 L ~ ( ) j y y jk( x, x j ), s. t. y 0 ; 0 for all 2 j 1 N (5) A non-lnear SVM s largely characterzed by the choce of ts kernel, and SVMs thus lnk the problems they are desgned for wth a large body of exstng work on kernel-based methods. Some common kernels functons nclude: Lnear kernel: k(x,x ) = (xx ) d Polynomal kernel: k ( x, x ) ( sx x c) 2 RBF kernel: k( x, x) exp( ( x x) ) Sgmod kernel: k( x, x ) tanh( s( x x) c) Once the kernel s fxed, SVM classfers have few user-chosen parameters. The best choce of kernel for a gven problem s stll a research ssue. Because the sze of the margn does not depend on the data dmenson, SVM are robust wth respect to data wth hgh nput dmenson. However, SVM are senstve to the presence of outlers, due to the regularzaton term for penalzng msclassfcaton (whch depends on the choce of C). The SVM algorthm requres O(n 2 ) storage and O(n 3 ) to learn. The SVM method can also be appled to the case of regresson. A verson of SVM for regresson, called support vector regresson (SVR), was proposed by Drucker et al. [1997]. The basc dea of SVR s that a non-lnear functon learns by a lnear learnng method n a kernel-nduced hgher dmensonal space. Smlarly to how SVM classfcaton gnores data ponts that are not support vectors, the SVR depend on a small subset of tranng data ponts. The SVM s major advantage les wth ther ablty to map varables onto an extremely hgh feature space. Ths, n essence facltates a means for the exploraton of nonlnear kernelbased classfers [Oladunn and Snghal, 2009; Burges, 1998], however, t has been dscovered they do not favour large datasets, due to the demands mposed on vrtual memory, and the tranng complexty resultant from the use of such a scaled collecton of data [Horng et al., 2010]. Work from Fe et al. [2008] hghlghted three crucal problems n the use of support vector machnes. These are attanng the optmal nput subset, correct kernel functon, and the optmal parameters of the selected kernel, all of whch are prme consderatons wthn ths study.

5 315 ITHEA Fg. 1. Maxmum-margn hyperplane for a SVM traned wth samples from two classes. Samples on the margn are support vectors Fg. 2. Kernel functon: a lnearly nseparable nput space can be mapped to a lnearly separable hgher-dmmensonal space Dataset and Preprocessng Ths study uses a dataset that contans data from physcal examnaton of patents, ncludng mammographc and sonographc examnatons, famly hstory of breast cancer, and personal hstory of breast malgnancy, all collected at Duke Unversty Medcal Centre [Jesneck et al., 2007]. Samples ncluded n the dataset are those selected for bopsy only f the lesons corresponded to sold masses on sonograms and f both mammographc and sonographc mages taken before the bopsy were avalable for revew. Data contan 803 samples, 296 of whch are malgnant and 507 bengn. Out of 39 descrptors, 13 are mammographc BI-RADS, 13 sonographc BI-RADS, 6 sonographc suggested by Stavros et al. [1995], 4 sonographc mass descrptors, and 3 patent hstory features. There s also a class label, -1 and 1, that ndcates f a sample s malgnant or bengn. The data features are as follows: mass sze, parenchyma densty, mass margn, mass shape, mass densty, calcfcaton number of partcles, calcfcaton dstrbuton, calcfcaton descrpton, archtectural dstorton, assocated fndngs, specal cases (as defned by the BI-RADS lexcon [BI-RADS, 2003]: asymmetrc tubular structure, ntramammary lymph node, global asymmetry, and focal asymmetry), comparson wth fndngs at pror examnaton, and change n mass sze. The sonographc features are radal dameter, antradal dameter, anteroposteror dameter, background tssue echo texture, mass shape, mass orentaton, mass margn, leson boundary, echo pattern, posteror acoustc features, calcfcatons wthn mass, specal cases (as defned by the BI- RADS lexcon: clustered mcrocysts, complcated cysts, mass n or on skn, foregn body,

6 Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 316 ntramammary lymph node, and axllary lymph node), and vascularty. The sx features suggested by Stavros [Stavros et al., 1995] are mass shape, mass margn, acoustc transmsson, thn echo pseudocapsule, mass echogencty, and calcfcatons. The four other sonographc mass descrptors are edge shadow, cystc component, and two mammographc BI-RADS descrptors appled to sonography mass shape (oval and lobulated are separate descrptors) and mass margn (replaces sonographc descrptor angular wth obscured). The three patent hstory features were famly hstory, patent age, and ndcaton for sonography [Jesneck et al., 2007]. Usng the dataset n ts orgnal format for classfcaton wth SVM would be problematc due to the large ampltude of feature values caused by the dfferent nature of the data varables and dfferent unts of measurements used. For example, the mass sze values range from 0 to 75, as long as calcfcaton ranges from 0 to 3. Such an nconsstency could affect the predctve abltes of a SVM classfer as some varables can be vewed as more nfluental than others. The approach we used to address that problem was to process each data varable (data column) separately by transformaton (6). It scales down the varables wthn the unt hypercube. We also explored how presence or absence of varables presented to the model for tranng and testng affects the classfer performance. Removng most rrelevant and redundant features from the data helps to allevate the effect of the curse of dmensonalty and to enhance the generalzaton capablty of the model, and to speed up the learnng process and to mprove the model nterpretablty. The feature selecton also helps to acqure better understandng about data and how they are related wth each other. The exhaustve search approach that consders all possble subsets of varables s best for datasets wth small cardnalty, but mpractcal for large number of features as n our case. Jesneck et al. [2007] proposed a feature subset of 14 descrptors (s14) for ther experments wth neural networks. They were derved by the stepwse feature selecton technque. There s no guarantee, however, that an optmal varable selecton for one classfcaton technque wll be optmal for another. In order to fnd the alternatve selectons for the SVM model we consdered several feature selecton algorthms, whch generally fall nto two categores: feature rankng and subset selecton. The latter s more advanced and wdely used n practce, whch made us focus on t. We consdered best frst, subset sze forward selecton, race search, scatter search and genetc search combned wth a set evaluaton technque that consders ndvdual predctve ablty of each feature along wth the degree of redundancy between them [Goldberg, 1989; (6)

7 317 ITHEA Hall, 1998]. We propose a set of 17 varables (s17) derved by the lnear forward selecton technque, proposed by Guetlen et al. [2009]. The feature set we obtaned conssts of the followng varables: patent age, ndcaton for sonography, mass margn, calcfcaton number of partcles, archtectural dstorton, anteroposteror dameter, mass shape, mass orentaton, leson boundary, specal cases, mass shape, mass margn, thn echo pseudocapsule, mass echogencty, edge shadow, cystc component, and mass margn. Two of these are general descrptors; three - mammographc BI-RADS; fve - sonographc BI-RADS; four - Stavros ; and three - sonographc mass descrptors. The feature set s relatvely balanced n representng dfferent categores of data. In our experments we also used the set of 14 varables (s14) mentoned above and the orgnal full set of 39 varables (s39). Emprcal Results and Dscusson Usng the tranng and testng datasets descrbed above, we bult a classfcaton model based on SVM. For the purposes of the ROC we used the support vector regresson technque, whch outputs predctons as real numbers between -1 and 1, whch mapped to the class labels (ether -1 or 1). In order to mnmze the bas n results assocated wth the random samplng of the tranng and testng data samples, we appled fve-fold crossvaldaton, a.k.a. rotaton estmaton. The dataset was randomly spt nto fve mutually exclusve subsets (folds) of equal sze. The model was traned and tested fve tmes so that each tme t was traned on one combnaton of four folds and tested on the remanng one. The cross-valdaton estmate of the overall model accuracy was calculated by (7). C V A 1 k k 1 A, (7) where the number of folds k=5, CVA s the cross-valdaton accuracy, and A s the accuracy measure of the -th fold (e.g. ht-rate, senstvty, specfcty). The prmary source for estmatng the model accuracy s the confuson matrx (a.k.a. contngency table), llustrated n Fgure 3. Results from experments were summarzed n four categores: true postves (TP), true negatves (TN), false postves (FP), and false negatves (FN). The numbers along the prmary dagonal n the matrx represent correct predctons, as long as those outsde the dagonal represent the errors.

8 Predcted Class Negatve Postve Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 318 Postve True Postve Count (TP) True Class Negatve False Postve Count (FP) False Negatve Count (FN) True Negatve Count (TN) Fg. 3. Confuson matrx for tabulaton of classfcaton results In order to estmate the model performance we used the followng dervatons from the confuson matrx: True Postve Rate (TPR), a.k.a. senstvty, ht rate, or recall s the rato of correctly classfed postves dvded by the total postve count. TPR= TP TP+ FN False postve rate (FPR), a.k.a. fall-out, or (1-specfcty) s the rato of ncorrectly classfed postves dvded by the total negatve count. FPR= FP FP+TN Accuracy (ACC) s the rato of correctly classfed nstances (both postves and negatves) dvded by the total number of nstances. ACC = TP+TN TP+TN + FP+ FN (8) (9) (10) Estmatng the accuracy of the bult SVM model s mportant for the followng two reasons: frst, t can be used to estmate the future predcton accuracy, whch could mply the level of confdence, the potental users may have; secondly, t can be used for choosng a partcular nstance of the SVM model among avalable optons, e.g. selecton of kernel functon and parameter settngs. Accuracy s a common performance estmator n machne learnng and data mnng, but n many cases and problem domans t s not suffcent metrc. Sometmes, accuracy can be

9 319 ITHEA msleadng, for example where mportant classes are underrepresented n the datasets and class dstrbuton s skewed. In that case accuracy s helpless n countng dfferent costs and consequences from msclassfcatons. Ths s the case of the doman we consder, as msdagnosed malgnant and bengn samples have dfferent consequences and even may cost lfe. Another drawback of the accuracy s that t depends on the classfer s operatng threshold. When SVM runs as a regresson functon that outputs real numbers between -1 and 1, mappng outputs to class labels requres defnng a threshold between -1 and 1, so that the output can fall below or above t,.e. mapped to one or another class label. Applyng dfferent thresholds produces dfferent nstances of the classfcaton model, each of whch features a specfc accuracy. In order to address those accuracy defcences, we dd Recever Operatng Characterstcs (ROC) analyss [Fawcett, 2006]. Ths s a graphcal assessment technque where the true postve rate s plotted on the Y-axs and false postve rate s plotted on the X-axs (Fgure 4). In the ROC space, a classfcaton model s a step curve plotted by connectng all model nstances made by varyng the threshold value. The lne that lnks (0,0) and (1,1) s the no-dscrmnaton lne. It represents the worst possble model, whch predcts by a completely random guess. Any other classfer should appear above that lne. If t pops below the lne, a negaton of ts predctons would move t above the no-dscrmnaton lne. On the other hand, the 'deal' classfer would be represented by the pont (0,1), the top-left corner, whch shows that all true postves are found and no false postves are found. Any model performance can be measured by ts proxmty to the 'deal' classfer. The closer the ROC curve s as a whole to the north-west corner, the better. That s also the most dstant from the no-dscrmnaton lne, the better. Gven a curve, the most northwest pont of the curve represents the model nstance wth maxmal accuracy. The ROC analyss also provdes means for quantfcaton of a model performance, these are Area Under the ROC Curve (AUC) and partal Area Under the ROC curve (pauc) where senstvty s above a certan value (p). The AUC / pauc are scalars that measure the overall model performance, regardless of the operatonal threshold. The bgger the values, the better the model s. As long as AUC provdes an overall estmaton of the model, the pauc s more relevant to the applcaton area, as the potental users of CAD tools are partcularly nterested n workng wth hgh levels of senstvty. It s beleved that senstvty above 90% ( 0.90AUC) s relevant to the applcaton feld. Another clncally relevant metrc that we estmated s specfcty at gven senstvty. In order to be consstent wth prevous studes [Jesneck et al., 2007], we consdered specfcty at 98% senstvty.

10 Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 320 Table 1 summarzes results from numerous experments where the SVM model was traned and tested usng four kernels: lnear, polynomal, RBF, and sgmod. For each of those kernels we expermented wth three dfferent sets of varables: s39 that contans all of them n the orgnal dataset and the selectons s14 and s17 dscussed above. Table 1. Performance of SVM wth lnear, polynomal, RBF, and sgmod kernels. Metrcs for comparson nclude: area under the ROC curve (AUC), partal AUC at senstvty above 90% (0.90AUC), specfcty at 98% senstvty, and maxmal accuracy (ACCmax). Models have been tested wth three varable selectons: s39, s17, and s14. Typcal radologst assessment values are also ncluded. Fgures n bold show best values. SVM lnear s39 s17 s14 Radologst SVM polynomal s39 s17 s14 Radologst AUC AUC AUC AUC Spec /98% sens Spec /98% sens ACCmax n/a ACCmax n/a SVM FBF s39 s17 s14 Radologst SVM sgmod s39 s17 s14 Radologst AUC AUC AUC AUC Spec /98% sens Spec /98% sens ACCmax n/a ACCmax n/a The table fgures show that the selecton of descrptors for tranng and testng plays a sgnfcant role n the SVM performance. Accordng to all metrcs and no matter whch kernel s selected, t s evdent that the varable set s14, proposed by Jesneck et al. [2007] for classfcaton wth MLP s outperformed by both s39 and s17. As mentoned before, that s not surprsng as an optmal varable selecton for one classfcaton model would not be optmal for another. We also show that the alternatve selecton of varables, s17, can outperform both s14 and s39. That selecton sgnfcantly mproves the 0.90AUC of s14 from 12% to 17%, dependng on whch kernel s used, and also outperforms the radologst value by 23%. The other clncally relevant metrc, specfcty at 89% senstvty, s also mproved by s17 n comparson wth s14 - from 6% to 16%. In some cases s39 performs as well as s17, but t never gets better. The varable set s17 shows tself as the best performer regardng AUC and ACC max wth only few exceptons.

11 321 ITHEA a) b) c) d) e) f) g) h) Fg. 4. Performance of SVM wth lnear, polynmal, RBF, and sgmod kernels wth three varable sets: all attrbutes (s39); selecton of 17 attrbutes based on the subset sze forward selecton method (s17) Guetln et al. [2009]; and selecton of 14 attrbutes proposed by Jesneck et al. [2007]

12 Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 322 We also explored how choce of the kernel functon nfluences the SVM predctve abltes. Ths s partcularly mportant when data belong to classes, whch are not lnearly separable. In those cases we can expect that the SVM model wth lnear kernel wouldn't perform well n contrast to the non-lnear ones. Table 1 shows the results for each kernel functon. The SVM works well wth all of them. Consderng s17 only, AUC for all four kernels s 91% and 0.90AUC s from 74% to 75%. Specfcty at hgh senstvty, however, vares wth dfferent kernels. Regardng ths metrc, the sgmod kernel outperforms the others, followed by the RBF, lnear, and polynomal. Our fndngs also could be compared wth those from studes that use models based on the most common neural networks - MLPs, gven that all methods use the same dataset and varable sets (Nachev & Stoyanov, 2010). SVM shows the same AUC as MLP, but mproves 0.90AUC by 7% (68% vs. 75%) and max accuracy by 2% (83% vs. 85%). Fg. 4 gves further detals on the SVM ROC analyss, The left-hand fgures llustrate the ROC curves and AUC for each kernel and varable set; the rght-hand fgures llustrate the area of senstvty above 90% and lst 0.90AUC. Concluson Ths study explores support vector machnes utlzed as predctors of malgnant breast masses, traned and tested wth data from mammographc and sonographc examnatons. We used data collected from Duke Unversty Medcal Centre, whch contans 39 descrptors. Our study was focused on two ssues: how reducton of dmensonalty of the tranng and testng data affect the dscrmnatory power of the model; and how choce of the SVM kernel functon and model parameters affect ts predctve abltes. In order to quantfy the model performance we dd ROC analyss and utlzed metrcs, such as true postve rate, false postve rate, area under the ROC curve, partal area under the ROC curve, and specfcty at hgh senstvty. Our results show that the reducton of dmensonalty plays a sgnfcant role n the model performance. We propose a set of 17 varables, whch outperforms the 14 varables set of Jesneck et al. [2007]. The choce kernel functon among lnear, polynomal, RBF, and sgmod, however, does not nfluences the model performance, wth excepton of one metrc - specfcty at hgh senstvty. In that case the sgmod kernel s the best performer. The fact that the lnear kernel shows smlar performance to that of the non-lnear kernels s an ndcaton that the feature space s lnearly separable and data ponts are dstrbuted n a way that makes the classfcaton task lnear n terms of complexty.

13 323 ITHEA We also found expermentally that the SVM outperform a common classfcaton technque used n the feld - MLP neural networks. SVM shows the same AUC, but mproves 0.90AUC by 7% (68% vs. 75%) and max accuracy by 2% (83% vs. 85%). In concluson, we beleve that SVM s a promsng technque for breast cancer dagnoss, but when used, t requres a careful reducton of dmensonalty and well-selected model parameters. Bblography [BI-RADS, 2003] Amercan College of Radology. BI-RADS: ultrasound, 1st ed. In: Breast magng reportng and data system: BI-RADS atlas, 4th ed. Reston, VA: Amercan College of Radology, 2003 [Burges, 1998] Burges, C. A Tutoral on Support Vector Machnes for Pattern Recognton. Data Mnng and Knowledge Dscovery, 2, , [Chen et al., 2009] Chen, S., Hsao, Y., Huang, Y., Kuo, S., Tseng, H., Wu, H., Chen, D.: Comparatve Analyss of Logstc Regresson, Support Vector Machne and Artfcal Neural Network for the Dfferental Dagnoss of Bengn and Malgnant Sold Breast Tumors by the Use of Three-Dmensonal Power Doppler Imagng. Korean J Radol vol. 10, [Drucker et al., 1997] Drucker, H. Burges, C., Kaufman, L., Smola, A., and Vapnk, V., Support vector regresson machnes, Advances n Neural Informaton Processng Systems 9, pages , Cambrdge, MA, MIT Press, [Fawcett, 2006] Fawcett, T. An ntroducton to ROC analyss ; Pattern Recognton Letters, Vol. 27 Issue 8, pp , [Fe et al., 2008] Fe, L., L, W. & Yong, H. Applcaton of least squares support vector machnes for dscrmnaton of red wne usng vsble and near nfrared spectroscopy. Intellgent System and Knowledge Engneerng, ISKE' 08, [Goldberg, 1989] Goldberg, D.: Genetc Algorthms n Search, Optmzaton, and Machne Learnng, Addson-Wesley: Readng, MA, [Guetlen et al., 2009] Guetlen, M., Frank, E., Hall, M., Karwath, A. Large Scale Attrbute Selecton Usng Wrappers ; In Proc. IEEE Symposum on CIDM, pp , [Hall et al., 2009] Hall, M., Frank, E., Holmes, G., Pfahrnger, B., Reutemann, P., and Wtten. I., The WEKA data mnng software: an update. SIGKDD Explor. Newsl. 11, 1, 10-18, [Horng et al., 2010] Horng, S., Su, M., Chen, Y., Kao, T., Chen, R., La, J. and Perkasa, C. A novel ntruson detecton system based on herarchcal clusterng and support vector machnes. Expert Systems wth Applcatons, 38, , [Jesneck et al., 2007] Jesneck, J., Lo, J., Baker, J. Breast Mass Lesons: Computer-Aded Dagnoss Models wth Mamographc and Sonographc Descrptors ; Radology, vol.244, Issue 2, pp , [Kopans, 1992] Kopans D. Standardzed mammographc reportng ; Radol Cln North Am, Vol. 30, pp , 1992 [Lacey et al., 2002] Lacey, J., Devesa, S., Brnton, L. Recent Trends n Breast Cancer Incdence and Mortalty. ; Envronmental and Molecular Mutageness, Vol. 39, pp , [Nachev & Stoyanov, 2010] Nachev, A. and Stoyanov, B., An Approach to Computer Aded Dagnoss by Mult-Layer Preceptrons, In Proceedngs of Internatonal Conference Artfcal Intellgence (IC-AI 10), Las Vegas, [Oladunn and Snghal, 2009] Oladunn, O. O. & Snghal, G Pecewse mult-classfcaton support vector machnes. Internatonal Jont Conference on Neural Networks, IJCNN'09, 2009.

14 Artfcal Intellgence Methods and Technques for Busness and Engneerng Applcatons 324 [Stavros et al., 1995] Stavros, A., Thckman, D., Rapp, C., Denns, M., Parker, S., Ssney, G. Sold Breast Modules: Use of Sonography to Destngush between Bengn and Malgnant Lesons ; Radology, Vol. 196, pp , [Vapnk, 1995] Vapnk, V., The Nature of Statstcal Learnng Theory. Sprnger, New York (1995) Authors' Informaton Anatol Nachev Busness Informaton Systems, Carnes Busness School, Natonal Unversty of Ireland, Galway, Ireland; e-mal: anatol.nachev@nugalway.e Major Felds of Scentfc Research: data mnng, neural networks, support vector machnes, adaptve resonance theory. Maread Hogan Busness Informaton Systems, Carnes Busness School, Natonal Unversty of Ireland, Galway, Ireland; e-mal: maread.hogan@nugalway.e Major Felds of Scentfc Research: HCI, usablty and accessblty n nformaton systems, data mnng.