Quantifying differences in feature importance rankings of #machinelearning #classification could enhance #interpretability and #explainability: we show how through the rank-biased overlap similarity measure. Take a look at my novel work!
https://link.springer.com/chapter/10.1007/978-3-031-15037-1_11
Check also my oral communication at the Brain Informatics 2022
Ever wondered how to quantitatively compare feature importance produced by Machine Learning algorithms?
In this new work presented at the Brain Informatics 2022, we introduce the Rank-Biased Overlap (RBO) as similarity measure for comparing rankings of features ordered by their importance. We used the automatic classification of Parkinson’s disease as case study.
Take a look at my recording if you are curious!
In case you missed the live, here the recording of the BI2022 Special Session on EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR UNVEILING THE BRAIN: FROM THE BLACK-BOX TO THE GLASS-BOX (XAIB)
15th July 2022, 14:00-16:00 (GMT+2), with Prof. Monica Hernandez, Dr. Bojan Bogdanovic and Dr. Antonio Parziale
https://drive.google.com/file/d/1yr_tbZ-9QXTQWHrkIlRi_9bohkGCEpzI/view?usp=sharing
Join us LIVE 15th July 14:00-16:00 GMT+2 on Google Meet at: https://meet.google.com/ogt-xpte-qek
The final program is here!
My Editorial for the Special Issue on “Machine Learning in Healthcare and Biomedical Application”
https://www.mdpi.com/1999-4893/15/3/97/htm
++++ CALL FOR PAPERS ++++
Application of Artificial Intelligence in Neurological Diseases
Dear Colleagues,
The wide application of artificial intelligence (AI) and machine learning (ML) on neuroscience data represents an unprecedented way for understanding neurological diseases. The AI and ML could, indeed, deal with multi-modal, multi-dimensional, and multi-source data, which can help to extract new knowledge about the pathological mechanisms that affect the human brain and, more generally, the nervous system. This Special Issue of the Journal of Personalized Medicine is devoted to collect original scientific articles that explore neurological diseases through the use of AI and ML approaches. In particular, we accept works that apply supervised and unsupervised learning, reinforcement learning, deep learning, and the more recent explainable and interpretable ML methodologies. Moreover, we seek to collect studies using and exploring different source of data, such as neuroimaging (structural MRI, functional MRI and Nirs), neurophysiology (TMS, EMG, EEG, MEG), biorobotics, and biomechanics (inertial, wearable, IoT sensors) applied to neurodegenerative diseases.
Contributions such as systematic reviews or meta-analyses on the above-mentioned topics are also welcome.
Dr. Alessia Sarica
Dr. Vera Gramigna
Dr. Maria Giovanna Bianco
Guest Editors
+++++CALL FOR PAPERS AND ABSTRACTS+++++
II Ed. EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR UNVEILING THE BRAIN: FROM THE BLACK-BOX TO THE GLASS-BOX.
BRAIN INFORMATICS 2022
#MachineLearning #ArtificialIntelligence #neuroscience
In case you missed the live, here the recording of the BI2021 Special Session on EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR UNVEILING THE BRAIN: FROM THE BLACK-BOX TO THE GLASS-BOX (XAIB)
18th September 2021, 14:00-16:00 UK time (GMT+1), with Dr. Rich Caruana, Dr. Michele Ferrante and Dr. Dimitris Pinotsis:
https://drive.google.com/file/d/1YHdJu_PHXH_s9To7dZQ66q2OFG4-6VNK/view?usp=sharing
A great virtual OHBM this year! I proposed the application of Interpretable Artificial Intelligence on Neuroimaging data through the Explainable Boosting Machine on Alzheimer’s data. Here my poster and a video presenting my work.
Alessia Sarica 