# A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes
## 1 Introduction
Since their method could express the uncertainty of a model in a graph, it enabled its intuitive evaluation. [PERSON] et al. devised a method to approximate and measure the expected value of the difference between confidence and accuracy [36]. Based on this study, [PERSON] et al. proposed a method that could estimate the calibration error more efficiently than existing methods [37]. However, previous studies were limited in their ability to measure the uncertainty in the model after learning was completed. Recently, research has been conducted to develop a model that can calibrate confidence by improving the training process. [PERSON] et al. and [PERSON] et al. tried to lower the uncertainty in the model by proposing a method to increase the diversity of representations through data mix-up [38; 39]. They argued that their proposed method could reduce the empirical risk of overfitting and overconfidence in the training data. Furthermore, [PERSON] et al. and [PERSON] et al. emphasized that uncalibrated confidence can be prevented by simply shifting the data's distribution [40; 41]. In addition, [PERSON] and [PERSON] proposed a method of measuring the calibration score for each object to determine the out-of-distribution that led to uncalibrated confidence [42]. [PERSON] et al. developed a regularized training method by assigning a penalty for overconfident predictions [43]. [PERSON] and [PERSON] calibrated the confidence of their predictive model by optimizing the loss function, reflecting the relationship between accuracy and uncertainty [44]. [PERSON] et al. considered model confidence as knowledge that can be obtained from data and proposed a neural network architecture that can learn it. They designed a novel learning strategy to calibrate confidence in modern predictive models with complex and deep layers, thereby lowering the uncertainty of deep neural networks [45]. [PERSON] et al. presented an interactive diagram that could visually represent both the uncertainty of individual observations and model confidence. In addition, they attempted various validations of the proposed method by conducting experiments on cases using both real-world and synthetic data [46]. Furthermore, [PERSON] et al. used various measures and histograms to comprehensively consider the model's capability, steadiness, accuracy, reliability, and fitness [47]. These previous studies had the advantage that they could reduce the uncertainty of various applications because they calibrated confidence during the model training process. We mainly focused on calibrating the confidence of TM by using the Variational Bayes (VB)-based generative model. Previous studies have argued that the confidence of the predictive model can be calibrated through the process of increasing the representation of the data. With this in mind, we propose a method of calibrating the confidence by (i) securing the representation of various data and (ii) generating data even when the number of training samples is small with a VB-based generative model. For that purpose, this study uses patent data to improve the problem with TM, which is a label-imbalanced and highly uncertain application. The patent system--the main subject of TM--encourages industrial development by disclosing instead of guaranteeing monopoly rights to inventors. A predictive model has been proposed for tasks such as technology transfer by extracting the features of TM contained in patents. [PERSON] et al. developed a deep learning-based framework to predict patent litigation [48]. In addition, [PERSON] argued that a machine learning model trained on a patent data could accurately and selectively estimate the technology to be transferred [49; 50]. Furthermore, [PERSON] et al. proposed a method that used a graph-based algorithm to determine the most efficient technology transfer path to promote TM innovation [51]. One limitation of these previous studies is that they did not consider potential uncertainty in patent analysis.