An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity

Alam, Shafiq; Ayub, Muhammad Sohaib; Arora, Sakshi; Khan, Muhammad Asad

doi:10.1016/j.dajour.2023.100341

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Recently, various transfer learning and data augmentation approaches have been investigated for text classification, such as those described by (Banerjee et al, 2019;Azam et al, 2022;González-Carvajal and Garrido-Merchán, 2020;Alam et al, 2023). These methods use pre-trained language models and fine-tune them on smaller datasets to enhance their performance on specific tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Prompt-Based and Standard Fine-Tuning for Urdu Text Classification

Ullah,

Azam,

Faheem

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Recent advancements in natural language processing have demonstrated the efficacy of pretrained language models for various downstream tasks through prompt-based fine-tuning. In contrast to standard fine-tuning, which relies solely on labeled examples, prompt-based finetuning combines a few labeled examples (few shot) with guidance through prompts tailored for the specific language and task. For lowresource languages, where labeled examples are limited, prompt-based fine-tuning appears to be a promising alternative. In this paper, we compare prompt-based and standard finetuning for the popular task of text classification in Urdu and Roman Urdu languages. We conduct experiments using five datasets, covering different domains, and pre-trained multilingual transformers. The results reveal that significant improvement of up to 13% in accuracy is achieved by prompt-based fine-tuning over standard fine-tuning approaches. This suggests the potential of prompt-based fine-tuning as a valuable approach for low-resource languages with limited labeled data.

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Section: Literature Reviewmentioning

confidence: 99%

Comparing Prompt-Based and Standard Fine-Tuning for Urdu Text Classification

Ullah,

Azam,

Faheem

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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“…Link prediction and imputation are among the most common techniques for addressing missing data in OSNs. Nevertheless, the e cacy of these methods may be constrained due to their heavy dependence on the interactions or connections between nodes for the estimation of missing data (Alam et al, 2023;Aziz et al, 2023;Mariani et al, 2020). Another widely adopted approach entails the creation of synthetic social networks if the real-world network is unavailable.…”

Section: Introductionmentioning

confidence: 99%

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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Social network analysis is a powerful tool for understanding various phenomena, but it requires data with explicit connections among users. However, such data is hard to obtain in real-time, especially from platforms like X, commonly known as Twitter, where users share topic-related content rather than personal connections. Therefore, this paper tackles a new problem of building a social network graph in real-time where explicit connections are unavailable. Our methodology is centred around the concept of user similarity as the fundamental basis for establishing connections, suggesting that users with similar characteristics are more likely to form connections. To implement this concept, we extracted easily accessible attributes from the Twitter platform and proposed a novel graph model based on similarity. We also introduce an Attribute-Weighted Euclidean Distance (AWED) to calculate user similarities. We compare the proposed graph with synthetic graphs based on network properties, online social network characteristics, and predictive analysis. The results suggest that the AWED graph provides a more precise representation of the dynamic connections that exist in real-world online social networks, surpassing the inherent constraints of synthetic graphs. We demonstrate that the proposed method of graph construction is simple, flexible, and effective for network analysis tasks.

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