[en] Nowadays, spam represents more than 45% of the world's email traffic. Filtering techniques to combat the problem of spam distribution have been the subject of many research studies in recent years. Several combinations of legal, administrative and technical perspectives were tested. The combination of technical approaches, namely, the widely exploited content-based and token-based filtering techniques, revealed low significance improvements on spam classification performance. Due to the limited performance of token-based strategies, new knowledge representation schemes (such as those based on word-embeddings, topics, or synsets) have been developed. The use of synsets to represent the meaning of the words guides the community towards the identification of the intentionality of a message, allowing the classification of messages that want to sell products, obtain information about us, etc. The advantage of this kind of synsets representations lies on the capability to taxonomically group concepts, handling the polysemy and synonymy. These properties have been successfully exploited in this research work to design a novel Machine Learning (ML) based lossless feature reduction schemes by grouping concepts strategies. This type of reduction schemes has achieved a reduction in the classification problem dimensionality (number of features), improving the classification performance. In a second step we introduce and demonstrate the effectiveness of a new feature reduction scheme that combines the strengths of lossless and lossy strategies. Finally, in order to use the Leetspeak encrypted words, a decoder has been designed and tested. The proposed system reduces the number of unprocessed words considerably, improving the classification rates of spam messages.

[eu] Gaur egun spam mezuek mundu osoko email trafiko globalaren %45-a suposatzen dute. Azken urteetan spam-aren arazoa konpontzeko tekniketan ikerketa ugari egin dira. Soluzio desberdinak probatu dira alderdi legalak, administratiboak eta teknikoak nahastuz. Ikuspuntu tekniko batetik edukietan eta token-etan oinarrituriko teknikek hobekuntza eskasak lortu dituzte. Azken hauek lortutako emaitzak hobetzeko, mezuen barruko informazioa errepresentatzeko era berriak garatu dira (adierazpen bektoriala, gaiak edo synset-ak). Hitzen esanahiak erabiltzeak mezua zein asmorekin idatzia izan den asmatzera bideratzen gaitu, produktuak saldu nahi dituen mezu bat bezala klasifikatuz, informazioa lortu nahi duen mezu bat bezala, etabar. Informazioa errepresentatzeko metodo berri hauek kontzeptuak elkartzeko gaitasuna daukate, esanahi desberdineko hitzak eta esanahi bereko hitzak taxonomikoki azteretuz. Propietate hauetan oinarrituz, ikerketa lan honetan, informazio galera gabeko ezaugarri kopuru murrizketa lortzen duen sistema bat garatu da, zein Ikaste Automatikoan oinarritzen den kontzeptuak elkartzeko. Honi esker arazoaren dimentsioa (tamaina) gutxitu da mezuen sailkapenaren errendimendua hobetuz. Bestalde, garaturiko lan honen abantailetan oinarritzen den bigarren sistema bat ere garatu da, non informazio galera gabeko sistemaren sendotasuna, informazio galera txiki batekin konbinatzen den. Amaitzeko Leetspeak-ean kodifikaturiko hitzen informazioa berreskuratzeko dekodifikatzaile bat garatu da. Garaturiko dekodifikatzaileak berreskuratzen dituen hitzen informazioari esker, klasifikazioaren emaitzak hobetu egiten dira.