BibTex format
@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}
}
RIS format (EndNote, RefMan)
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 -