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Subgroup Discovery for Defect Prediction

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Subgroup Discovery for Defect Prediction

Author: Rodríguez García, Daniel; Ruiz Sánchez, Roberto; Riquelme Santos, José Cristóbal; Harrison, Rachel
Publisher: Springer
Year: 2011
DOI: 10.1007/978-3-642-23716-4_25
Source: https://idus.us.es/bitstreams/ddaa5346-50f4-445c-aa32-ee671dba288b/download
Subg oup Disco e y o De ec P edic ion
Daniel Rod ´ıguez1,R.Ruiz
2, J.C. Riquelme3, and Rachel Ha ison4
1Uni . o Alcal´a, 28871 Alcal´a, Spain
[email p o ec ed]
2Pablo de Ola ide Uni ., 41013 Se ille, Spain
[email p o ec ed]
3Uni . o Se ille, 41012 Se ille, Spain
[email p o ec ed]
4Ox o d B ookes Uni ., Ox o d OX33 1HX, UK
[email p o ec ed]
Al hough he e is ex ensi e li e a u e in so wa e de ec p edic ion echniques,
machine lea ning app oaches ha e ye o be ully explo ed and in pa icula ,
Subg oup Disco e y (SD) echniques. SD algo i hms aim o find subg oups o
da a ha a e s a is ically diffe en gi en a p ope y o in e es [1,2]. SD lies
be ween p edic i e (finding ules gi en his o ical da a and a p ope y o in e es )
and desc ip i e asks (disco e ing in e es ing pa e ns in da a). An impo an
diffe ence wi h classifica ion asks is ha he SD algo i hms only ocus on finding
subg oups (e.g., inducing ules) o he p ope y o in e es and do no necessa ily
desc ibe all ins ances in he da ase .
In his p elimina y s udy, we ha e compa ed wo well-known algo i hms, he
Subg oup Disco e y algo i hm [3] and CN2-SD algo i hm [4], by applying hem
o se e al da ase s om he publicly a ailable PROMISE eposi o y [5], as well
as he Bug P edic ion Da ase c ea ed by D’Amb os e al. [6]. The compa ison
is pe o med using quali y measu es adap ed om classifica ion measu es. The
esul s show ha gene a ed models can be used o guide es ing effo . The
pa ame e s o he SD algo i hms can be adjus ed o balance he specifici y and
gene ali y o a ule so ha he selec ed ules can be conside ed good enough o
so wa e enginee ing s anda ds. The induced ules a e simple o use and easy
o unde s and. Fu he wo k wi h mo e da ase s and o he SD algo i hms ha
ackle he disco e y o subg oups using diffe en app oaches (e.g., con inuous
a ibu es, disc e iza ion, quali y measu es, e c.) is needed.
Re e ences
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o he 1s Eu opean Symposium on P inciples o Da a Mining, pp. 78–87 (1997)
2. He e a, F., Ca mona del Jesus, C.J., Gonz´alez, P., del Jesus, M.J.: An o e iew on
subg oup disco e y: Founda ions and applica ions. Knowl. In . Sys . (2010)
Resea ch suppo ed by Spanish Minis y o Educa ion MEC TIN 2007-68084-C02.
3. Gambe ge , D., La ac, N.: Expe -guided subg oup disco e y: me hodology and
applica ion. Jou nal o A ificial In elligence Resea ch 17, 501–527 (2002)
4. La aˇc, N., Ka ˇsek, B., Flach, P., Todo o ski, L.: Subg oup disco e y wi h CN2-SD.
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enginee ing da a. Wes Vi ginia Uni e si y, Depa men o Compu e Science (2007)
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