3,253 research outputs found
A stable gene selection in microarray data analysis
BACKGROUND: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. RESULTS: Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets. CONCLUSION: The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced
Effective Distillation of Table-based Reasoning Ability from LLMs
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their enormous parameter size and extremely high requirements for compute power pose challenges for their practical deployment. Recent research has revealed that specific capabilities of LLMs, such as numerical reasoning, can be transferred to smaller models through distillation. Some studies explore the potential of leveraging LLMs to perform table-based reasoning. However, there has been no prior work focusing on table reasoning skills in smaller models specifically tailored for scientific table-to-text generation tasks. In this paper, we propose a novel table-based reasoning distillation approach, with the aim of distilling LLMs into tailored smaller models. Our experimental results have shown that a 220 million parameter model (Flan-T5-base) fine-tuned using distilled data, not only achieves a significant improvement compared to traditionally fine-tuned baselines, but also surpasses specific LLMs on a scientific table-to-text generation dataset. Our code is available at https://github.com/Bernard-Yang/DistillTableCoT
Effective Distillation of Table-based Reasoning Ability from LLMs
Large Language Models (LLMs) have demonstrated remarkable performance across
a wide range of natural language processing tasks. However, their remarkable
parameter size and their impressive high requirement of computing resources
pose challenges for their practical deployment. Recent research has revealed
that specific capabilities of LLMs, such as numerical reasoning, can be
transferred to smaller models through distillation. Some studies explore the
potential of leveraging LLMs to perform table-based reasoning. Nevertheless,
prior to our work, there has been no investigation into the prospect of
specialising table reasoning skills in smaller models specifically tailored for
table-to-text generation tasks. In this paper, we propose a novel table-based
reasoning distillation, with the aim of distilling distilling LLMs into
tailored, smaller models specifically designed for table-based reasoning task.
Experimental results have shown that a 0.22 billion parameter model
(Flan-T5-base) fine-tuned using distilled data, not only achieves a significant
improvement compared to traditionally fine-tuned baselines but also surpasses
specific LLMs like gpt-3.5-turbo on the scientific table-to-text generation
dataset (SciGen). The code and data are released in
https://github.com/Bernard-Yang/TableDistill
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