@inproceedings{hossain-2026-mlenthusiast,
title = "mlenthusiast at {N}akba{A}rchive{C}lassifier Shared Task: A Lightweight {SVM}-Gated Ensemble of {E}fficient{N}ets for Image Classification",
author = "Hossain, Md. Ajwad",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.31/",
doi = "10.63317/5aw7xdt47vzs",
pages = "217--220",
abstract = "Image classification under strict time constraints requires a delicate balance between feature complexity and computational overhead. This paper presents an optimized ensemble methodology developed for the NAKABA competition, focusing on identifying structural destruction. We propose a hybrid architecture that leverages two distinct Convolutional Neural Networks (EfficientNetB0 and EfficientNetB3) as base feature extractors, coupled with a Support Vector Machine (SVM) functioning as a meta-classifier. Instead of standard probability averaging or processing high-dimensional embeddings directly, the Meta-SVM acts as a learned gating mechanism to optimally combine the low-dimensional probability predictions of the base models. This ensures robust performance without the latency of heavier deep learning architectures. Empirical results demonstrate the efficacy of this approach. The model achieved a validation accuracy of 0.884 and a weighted F1-score of 0.885, with a notable F1-score of 0.839 on the challenging `destruction' class. On the official NAKABA leaderboard test set, the ensemble maintained strong generalization, achieving an F1-score of 0.831 and an accuracy of 0.845, which secured the 12th position overall and proved the model{'}s high effectiveness within the competition{'}s strict operational constraints."
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<abstract>Image classification under strict time constraints requires a delicate balance between feature complexity and computational overhead. This paper presents an optimized ensemble methodology developed for the NAKABA competition, focusing on identifying structural destruction. We propose a hybrid architecture that leverages two distinct Convolutional Neural Networks (EfficientNetB0 and EfficientNetB3) as base feature extractors, coupled with a Support Vector Machine (SVM) functioning as a meta-classifier. Instead of standard probability averaging or processing high-dimensional embeddings directly, the Meta-SVM acts as a learned gating mechanism to optimally combine the low-dimensional probability predictions of the base models. This ensures robust performance without the latency of heavier deep learning architectures. Empirical results demonstrate the efficacy of this approach. The model achieved a validation accuracy of 0.884 and a weighted F1-score of 0.885, with a notable F1-score of 0.839 on the challenging ‘destruction’ class. On the official NAKABA leaderboard test set, the ensemble maintained strong generalization, achieving an F1-score of 0.831 and an accuracy of 0.845, which secured the 12th position overall and proved the model’s high effectiveness within the competition’s strict operational constraints.</abstract>
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%0 Conference Proceedings
%T mlenthusiast at NakbaArchiveClassifier Shared Task: A Lightweight SVM-Gated Ensemble of EfficientNets for Image Classification
%A Hossain, Md. Ajwad
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F hossain-2026-mlenthusiast
%X Image classification under strict time constraints requires a delicate balance between feature complexity and computational overhead. This paper presents an optimized ensemble methodology developed for the NAKABA competition, focusing on identifying structural destruction. We propose a hybrid architecture that leverages two distinct Convolutional Neural Networks (EfficientNetB0 and EfficientNetB3) as base feature extractors, coupled with a Support Vector Machine (SVM) functioning as a meta-classifier. Instead of standard probability averaging or processing high-dimensional embeddings directly, the Meta-SVM acts as a learned gating mechanism to optimally combine the low-dimensional probability predictions of the base models. This ensures robust performance without the latency of heavier deep learning architectures. Empirical results demonstrate the efficacy of this approach. The model achieved a validation accuracy of 0.884 and a weighted F1-score of 0.885, with a notable F1-score of 0.839 on the challenging ‘destruction’ class. On the official NAKABA leaderboard test set, the ensemble maintained strong generalization, achieving an F1-score of 0.831 and an accuracy of 0.845, which secured the 12th position overall and proved the model’s high effectiveness within the competition’s strict operational constraints.
%R 10.63317/5aw7xdt47vzs
%U https://aclanthology.org/2026.nakbanlp-1.31/
%U https://doi.org/10.63317/5aw7xdt47vzs
%P 217-220
Markdown (Informal)
[mlenthusiast at NakbaArchiveClassifier Shared Task: A Lightweight SVM-Gated Ensemble of EfficientNets for Image Classification](https://aclanthology.org/2026.nakbanlp-1.31/) (Hossain, NakbaNLP 2026)
ACL