A Hybrid Approach To Pattern Classification Using Neural Networks and Defeasible Argumentation

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1, . . . cm modeling some concept C results as an output, such that every cluster ci is labeled as positive or negative. In such a setting clusters can overlap, and a new unlabeled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper introduces a novel, hybrid approach to solve the above problem by combining a neural network N along with a background theory T specified in defeasible logic programming (DeLP) which models preference criteria for performing clustering.

[1]  Guido Governatori,et al.  An algorithm for the induction of defeasible logic theories from databases , 2003, ADC.

[2]  Yvon Savaria,et al.  Generalization, discrimination, and multiple categorization using adaptive resonance theory , 1999, IEEE Trans. Neural Networks.

[3]  Timo Honkela,et al.  Self-Organizing Maps In Natural Language Processing , 1997 .

[4]  Valluru Rao,et al.  C++ neural networks and fuzzy logic , 1993 .

[5]  Guillermo Ricardo Simari,et al.  Pruning Search Space in Defeasible Argumentation , 2004, cs/0405106.

[6]  Dov M. Gabbay,et al.  Handbook of Philosophical Logic , 2002 .

[7]  Ana Gabriela Maguitman,et al.  Logical models of argument , 2000, CSUR.

[8]  Pattie Maes,et al.  Collaborative Interface Agents , 1994, AAAI.

[9]  Henry Prakken,et al.  The Role of Logic in Computational Models of Legal Argument: A Critical Survey , 2002, Computational Logic: Logic Programming and Beyond.

[10]  Bart Verheij,et al.  Artificial argument assistants for defeasible argumentation , 2003, Artif. Intell..

[11]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[12]  Antonis C. Kakas,et al.  Computational Logic: Logic Programming and Beyond , 2002, Lecture Notes in Computer Science.

[13]  Giovanni Da San Martino.  Self-Organizing Maps in Natural Language Processing , 2003 .

[14]  Guillermo Ricardo Simari,et al.  A Mathematical Treatment of Defeasible Reasoning and its Implementation , 1992, Artif. Intell..

[15]  Anthony Hunter,et al.  Hybrid argumentation systems for structured news reports , 2001, The Knowledge Engineering Review.

[16]  Jude Shavlik,et al.  An Approach to Combining Explanation-based and Neural Learning Algorithms , 1989 .

[17]  Guillermo Ricardo Simari,et al.  Computing Generalized Specificity , 2003, J. Appl. Non Class. Logics.

[18]  Ian Davidson,et al.  Visualizing Clustering Results , 2002, SDM.

[19]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[20]  David Stuart Robertson,et al.  Argument-based applications to knowledge engineering , 2000, The Knowledge Engineering Review.

[21]  Sergio Alejandro Gómez,et al.  QUERANDO!: un agente de filtrado de documentos web , 2001 .

[22]  David M. Skapura,et al.  Building neural networks , 1995 .

[23]  Guillermo Ricardo Simari,et al.  Defeasible logic programming: an argumentative approach , 2003, Theory and Practice of Logic Programming.