Ibna Kowsar

Ibna Kowsar, Kazi F. Akhter, Manar D. Samad

Transfer learning of tabular data is non-trivial due to heterogeneity in the feature space across disparate domains. The limited success of traditional deep learning in tabular knowledge transfer can be advanced by leveraging large language models (LLMs). However, the efficacy of LLMs often stagnates for mixed data types structured in tables due to the limitations of text prompts and in-context learning. We propose a lightweight transfer learning framework that fine-tunes an LLM using source tabular data and transplants the LLM's selective $key$ and $value$ projection weights into a gated feature tokenized transformer (gFTT) built for tabular data. The gFTT model with cross-domain attention is fine-tuned using target tabular data for transfer learning, eliminating the need for shared features, LLM prompt engineering, and large-scale pretrained models. Our experiments using ten pairs of source-target data sets and 12 baselines demonstrate the superiority of the proposed LLM-attention transplant for transfer learning (LATTLE) method over traditional ML models, state-of-the-art deep tabular architectures, and transfer learning models trained on thousands to billions of tabular samples. The proposed attention transfer demonstrates an effective solution to learning relationships between data tables using an LLM in a low-resource learning environment. The source code for the proposed method is publicly available.

Shourav B. Rabbani, Ibna Kowsar, Manar D. Samad

Despite the artificial intelligence (AI) revolution, deep learning has yet to achieve much success with tabular data due to heterogeneous feature space and limited sample sizes without viable transfer learning. The new era of generative AI, powered by large language models (LLM), brings unprecedented learning opportunities to diverse data and domains. This paper investigates the effectiveness of an LLM application programming interface (API) and transfer learning of LLM in tabular data classification. LLM APIs respond to input text prompts with tokenized data and instructions, whereas transfer learning finetunes an LLM for a target classification task. This paper proposes an end-to-end finetuning of LLM to demonstrate cross-data transfer learning on ten benchmark data sets when large pre-trained tabular data models do not exist to facilitate transfer learning. The proposed LLM finetuning method outperforms state-of-the-art machine and deep learning methods on tabular data with less than ten features - a standard feature size for tabular data sets. The transfer learning approach uses a fraction of the computational cost of other deep learning or API-based solutions while ensuring competitive or superior classification performance.

Ibna Kowsar, Shourav B. Rabbani, Yina Hou et al.

Missing values of varying patterns and rates in real-world tabular data pose a significant challenge in developing reliable data-driven models. The most commonly used statistical and machine learning methods for missing value imputation may be ineffective when the missing rate is high and not random. This paper explores row and column attention in tabular data as between-feature and between-sample attention in a novel framework to reconstruct missing values. The proposed method uses CutMix data augmentation within a contrastive learning framework to improve the uncertainty of missing value estimation. The performance and generalizability of trained imputation models are evaluated in set-aside test data folds with missing values. The proposed framework is compared with 11 state-of-the-art statistical, machine learning, and deep imputation methods using 12 diverse tabular data sets. The average performance rank of our proposed method demonstrates its superiority over the state-of-the-art methods for missing rates between 10% and 90% and three missing value types, especially when the missing values are not random. The quality of the imputed data using our proposed method is compared in a downstream patient classification task using real-world electronic health records. This paper highlights the heterogeneity of tabular data sets to recommend imputation methods based on missing value types and data characteristics.

Manar D. Samad, Kazi Fuad B. Akhter, Shourav B. Rabbani et al.

Tabular data sets with varying missing values are prepared for machine learning using an arbitrary imputation strategy. Synthetic values generated by imputation models often raise concerns about data quality and the reliability of data-driven outcomes. To address these concerns, this article proposes an imputation-free incremental attention learning (IFIAL) method for tabular data. A pair of attention masks is derived and retrofitted to a transformer to directly streamline tabular data without imputing or initializing missing values. The proposed method incrementally learns partitions of overlapping and fixed-size feature sets to enhance the efficiency and performance of the transformer. The average classification performance rank order across 17 diverse tabular data sets highlights the superiority of IFIAL over 11 state-of-the-art learning methods with or without missing value imputations. Further experiments substantiate the robustness of IFIAL against varying missing value types and rates compared to methods involving missing value imputation. Our analysis reveals that a feature partition size of half the original feature space is, both computationally and in terms of accuracy, the best choice for the proposed incremental learning. The proposed method is one of the first solutions to enable deep attention learning of tabular data without requiring missing-value imputation. The source code for this paper is publicly available.