Version [2.0] - [AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data]
Moncef Garouani,
Mourad Bouneffa,
Adeel Ahmad,
Mohamed Hamlich
Affiliations
Moncef Garouani
Univ. Littoral Cote d’Opale, UR 4491, LISIC, Laboratoire d’Informatique Signal et Image de la Cote d’Opale, F-62100 Calais, France; CCPS Laboratory, ENSAM, University of Hassan II, Casablanca, Morocco; Study and Research Center for Engineering and Management(CERIM), HESTIM, Casablanca, Morocco; Corresponding author at: Univ. Littoral Cote d’Opale, UR 4491, LISIC, Laboratoire d’Informatique Signal et Image de la Cote d’Opale, F-62100 Calais, France.
Mourad Bouneffa
Univ. Littoral Cote d’Opale, UR 4491, LISIC, Laboratoire d’Informatique Signal et Image de la Cote d’Opale, F-62100 Calais, France
Adeel Ahmad
Univ. Littoral Cote d’Opale, UR 4491, LISIC, Laboratoire d’Informatique Signal et Image de la Cote d’Opale, F-62100 Calais, France
Mohamed Hamlich
CCPS Laboratory, ENSAM, University of Hassan II, Casablanca, Morocco
We report a new release of the self-explainable AutoML software AMLBID. The software package is a meta-learning based AutoML decision support system that assists machine learning(ML) experts and neophytes in building well performing ML pipelines and makes the outcomes transparent and interpretable. The release 2.0 of the software introduces an enhanced efficiency of algorithms recommendation. The performance improvement is mainly achieved by the integration of the AeKNN meta-model. This implementation makes datasets meta-features more informative and further improve the accuracy. The release 2.0 also introduces the standalone AMLBIDesc, a desktop version of the AMLBID software which makes the tool more accessible to non-expert users.