Eli lici citin ing Fu Fuzz zzy Kn Knowl wledg dge e fr from - PowerPoint PPT Presentation

Eli lici citin ing Fu Fuzz zzy Kn Knowl wledg dge e fr from th the PI PIMA MA Dataset Antonio d’Acierno ISA A – CNR daci cier erno.a@ no.a@is isa.cnr a.cnr.it .it Giuseppe eppe De Pietro ro, , Massimo mo Esposit sito ICAR R – CNR giuseppe.depiet eppe.depietro@ ro@na. na.icar.c icar.cnr nr.it .it massimo imo.espo .esposito sito@na.i @na.icar.cnr.it car.cnr.it

Our wor ork  Rece cent ntly, ly, we prop opose osed d a six-steps eps a data driven en metho hodolo dology y to automa mati ticall cally y build fuzzy inferen ence ce systems ems [6]. – The methodo odology logy produ duce ces s FIS with h an user define ned number ber of rules. s. – Each ch step can be approa roach ched ed using g several al strategi egies es  In this paper er, , we use an implement ementati ation on of our metho hodo dolo logy gy to elici cit t knowledge ledge from m the PIMA dataset et  We obtai tain: n: – an interesting sting perfor ormance mance in terms of correct ect class ssifi ificat cation ion rate – linguis guistic tic varia iables les are likely ly to be easily ily understoo tood from m huma man beings. ngs. [6] A. d’Acierno, G. De Pietro, M. Esposito. Data Driven Generation of Fuzzy Systems: An Application to Breast Cancer Detecti ction, on, 7th Internationa onal Meeting g on Computa putationa onal Intelligen ence ce Method ods for Bioinfor orma matics cs and Biosta tatis tistics (CIBB BB 2010), ), Palermo mo (Ita (Italy), ), 16-18 8 Septe temb mber r 2010. 2 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

Intr trod oductio uction  To increas ase e the chan ange of su success ssful ul treat atments, ts, ear arly y de detectio ion n of al almost st an any di dise seas ase is a s a ke key fac acto tor.  The de detectio ion can an be be often formula late ted d as as a bi a binar ary de decisi sion mak aking probl blem: m: – unce cert rtaint nty y in form of informat rmation ion inco complet mpleten eness ess, , impre reci cisene seness ss, , fragmen gmentar tarines ness, s, not fully reliabil bilit ity, y, vagu guen eness ess and co contr tradi adictor ctorine iness ss often en affects cts these e probl oblems. ems.  Compute uterize rized d di diag agnost stic c tools s to su support physi sicia cians ns in interpretin eting g da data a hav ave be been thus s de developed – Diagnos gnosti tic c Deci cisio ion n suppor ort t Systems ems (DDSS) S) 3 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

DD DDSS A diagnos nosti tic c tool l should posses sess s [1] three ee (often en in  co cont ntras rast) t) ch character acteristic stics: s: it must attain ain the best t possib ible le perfor ormance mance in terms of correct ct – clas assi sification fication rate. It would ld be desirable able the system em not only provide des s a diagnosis nosis but – also so a numerica rical l value ue represe senting nting the degree to which ich the system em is is confid nfiden ent t in the solution. tion. It would ld be also o useful ul if the phys ysici ician n is not face ced d with h a blac ack k box – that at simply ly output puts s answe wers rs but the system em should uld provid ide some insig ight into how the solution ution has been derived (inter erpre preta tabil ility). ity). [1] C. A. Pena-Reyes Reyes and M. M. Sipper. . A fuzzy-genet etic approach oach to breast st cancer er diagnosis. osis. Artificial cial Intelligenc ence e in Medicine, ne, 17(2):13 131 – 155, 55, 1999. 4 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

DD DDSS  Di Diag agnost stic ic tools, s, however, , typically ally hav ave unequal al clas assi sificatio fication error cost sts s so so th that at st strai aight ht CR can annot be as be assu sumed d as as a ca a careful ul meas asure of the goodn dness ss of the clas assi sifie fier. r.  A Receiv iver er Op Operat atin ing Char aracte acteris ristic tic (ROC OC) grap aph has as be been sh showed d to be be a m a more ac accurate ate techniqu ique e for se select cting ing clas assi sifie fiers s ba base sed d on th their performance ance. nce  can  We guess ss that at al also so the confide dence an be be use sed f d for se select cting ing clas assi sifie fier si since a g a good d clas assi sifie fier sh should d be be highly hly confide dent nt with correctly tly clas assi sifi fied d exa xamples es while it should be “doubtful” with misclassified data points. s. 5 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

FI FIS  A Fuzzy Infere renc nce e System em (FIS) ) is a system m that (tries es to) solve ve a (typical ically ly co compl plex ex and nonline inear) ar) problem blem by utilizing zing fuzzy logic ic metho hodo dologi logies es and d it is co composed posed of a fuzzifi ifier r (transl nslate tes s real- 1. 1. value lued d inputs ts into fuzzy y value lues) s) an inference ence engine e (applie plies s 2. 2. a fuzzy y reason soning ing mech chani anism sm to obtain tain a fuzzy zy output), put), a defuzzif ifie ier r (transl nslates es 3. 3. this s latter r output ut into a crisp sp value), ue), of a knowl wledge dge base e 4. 4. (containing ontaining both th rules and memb mber ership ship funct ctio ions). s). 6 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

FI FIS  The inference nce process ess is performed rmed by the engine ne using g the rules s conta taine ined in the rule base se if antece cede dent nt then consequent equent  The antece cedent ent is a fuzzy-logic logic express ssion ion composed posed of one or more simple ple fuzzy y expressi sions s connect ected ed by fuzzy y operators ators (the fuzzy zy equivale ivalent nt of the class ssical ical and, or and not), ,  In Mamdani dani systems ems, the consequent equent is an express ssion ion that t assig igns ns fuzzy zy values ues to the output: put: if service ce is good then tip is average age  In Takag kagi-Sugeno(TS) Sugeno(TS) syste tems, ms, the conse sequ quent nt express sses s output ut variab iable les s as a function tion that t maps s the input space ce into the output ut space: ce: if service ce is good then tip = f(serv rvice) ice) wher ere e f is (typically pically) a first t order linear ear function ction that t becomes omes a consta stant nt in zero-order rder TS systems. tems. 7 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

Fu Fuzz zzy y mo modeling  Fuzzy model eling ing is the task of identify ifying ng the paramet meter ers of a FIS so that a desired ed behavior vior is attaine ined. .  Knowl owledge edge dr driven en appro roach ch: – When en the availab ilable le knowled ledge ge is complete lete and the problem lem space ce is not very large e the system tem can be constru tructe cted d directly ly using ing knowl owledge dge elicited ted from m huma man experts. s.  Alter ernati natively vely, , data driven n fuzzy modeli ling ng ca can be used: – Avail ailab able le data a and AI technologies nologies are used to build ld the rules and/or or memb mber ership ship funct ctio ions. s.  A probl blem: m: the knowledg dge e ba base se generat ated d au automati atically cally from da data a may ay not be be fully y interpre pretable. table. 8 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it

Inte terpr pretab etabili ility ty Three e co condit itio ions ns ca can be defined ed to obtain ain an interpr rpretabl etable e  fuzzy y model l [5]: the fuzzy sets ca can be interpr rpreted eted as linguistic stic labels ls (low, , 1. 1. medium, um, high, , medium-lo low, w, etc) c); the set of rules must be as small as possible ible; ; 2. 2. the if-part rt of the rules should be derived ed from m a subset t of 3. 3. indepe ependen ndent t variables ables rather er than from m the full set. Inter erpre retabi tabilit ity y is a key feature ure in a DDSS. S.  Serge ge Guillaume. e. Designi ning ng fuzzy inferen ence ce systems from data: : An interpre retabi ability- 5. 5. orient nted ed review. . IEEE Transac sactions ons on Fuzzy Systems, s, 9(3):426 26 – 44 443, , 2001. 9 Joint NETTAB 2010 and BBCC 2010, Naples (Italy), November 29 – December 1, 2010, Antonio d’Acierno, dacierno.a@isa.cnr.it