Plot shows the proportion of samples associated with each odor class in Integrated Dataset (IGD) and Subset of Integrated Dataset(Subset-IGD).

Plot shows the proportion of samples associated with each odor class in Integrated Dataset (IGD) and Subset of Integrated Dataset(Subset-IGD).

<p>There is a significant imbalance in associated samples of odor labels, with the “fruity ”label appearing around 27% fthe time, whereas the occurrence of “fennel” is less than 1% in the dataset.</p>

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Bibliographic Details
Main Author:	Durgesh Ameta (21439753) (author)
Other Authors:	Surendra Kumar (33312) (author), Rishav Mishra (21439756) (author), Laxmidhar Behera (14341372) (author), Aniruddha Chakraborty (394000) (author), Tushar Sandhan (21439759) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified use machine learning systematic study compares sensitive model outperform selected representation method random resampling techniques proposed resampling techniques mld )&# 8212 deep learning approaches multimodal features obtained exploring feature performance based features compared direct comparative studies odor classification multi label classification models three mentioned features large novel dataset explainable model sheds molecular fingerprint features odor classification molecular fingerprint three kinds predictive performance label datasets different features recent studies previous studies molecular fingerprints xlink "> vibrational spectra usually represented two gaps sensory modality second objective research delves remains complex odour relations multifaceted approach inherent challenge inadequately understood inadequately documented fp ). first one electric nose data imbalance csmlp ). class imbalance also report
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