1. Comments on 'Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection'
- Author
-
Bayu Adhi Tama and Seung-Chul Lee
- Subjects
0209 industrial biotechnology ,Computer science ,media_common.quotation_subject ,Stacking ,02 engineering and technology ,computer.software_genre ,Machine learning ,020901 industrial engineering & automation ,Artificial Intelligence ,Reading (process) ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,media_common ,Quantitative Biology::Neurons and Cognition ,Artificial neural network ,business.industry ,General Engineering ,Base (topology) ,Expert system ,Computer Science Applications ,ComputingMethodologies_PATTERNRECOGNITION ,Seizure detection ,Deep neural networks ,020201 artificial intelligence & image processing ,Epileptic seizure ,Artificial intelligence ,medicine.symptom ,business ,computer - Abstract
This short communication provides a discourse emerged after reading “Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection, Expert Systems with Applications, 148, 113239, 2020.” The discussed paper proposes a novel application of stacking-based ensemble for seizure detection, where several deep neural networks were used as base classifiers. The ensemble design and experimental results presented by the author show some weaknesses, which is indicated by, one of which, an inability of the proposed model to outperform previous studies. In this note, controversy of the discussed paper is explained and an improved version of stacking-based deep neural network is also further introduced and detailed to prevent it in the future.
- Published
- 2021