@inproceedings{Cocarascu:2018,
author = {Cocarascu, O and Cyras, K and Toni, F},
title = {Explanatory predictions with artificial neural networks and argumentation},
url = {http://hdl.handle.net/10044/1/62202},
year = {2018}
}

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TY  - CPAPER
AB  - Data-centric  AI  has  proven  successful  in  severaldomains, but its outputs are often hard to explain.We  present  an  architecture  combining  ArtificialNeural Networks (ANNs) for feature selection andan  instance  of  Abstract  Argumentation  (AA)  forreasoning to provide effective predictions, explain-able both dialectically and logically.  In particular,we train an autoencoder to rank features in input ex-amples, and select highest-ranked features to gen-erate an AA framework that can be used for mak-ing and explaining predictions as well as mappedonto logical rules, which can equivalently be usedfor  making  predictions  and  for  explaining.Weshow empirically that our method significantly out-performs ANNs and a decision-tree-based methodfrom which logical rules can also be extracted.
AU  - Cocarascu,O
AU  - Cyras,K
AU  - Toni,F
PY  - 2018///
TI  - Explanatory predictions with artificial neural networks and argumentation
UR  - http://hdl.handle.net/10044/1/62202
ER  - 

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