@techreport{oai:ipsj.ixsq.nii.ac.jp:02001559,
 author = {與倉,蕉真 and 一木,輝久 and Shoma Yokura and Akihisa Ichiki},
 issue = {5},
 month = {Mar},
 note = {二値分類とはデータを異なる二つのクラスのいずれかに分類するタスクであり多くの分野で応用されている．しかし，通常の分類器では二つの分布の重なり部分に属するデータや分布外データに対して過剰な予測を行う傾向にあるため，ハイリスクが付きまとう分野では適用するべきではない．この問題に対処するために不確実性の定量化と意思決定アプローチ方法で多くの手法が提案されているが，リサンプリングやモデルの改良，閾値の最適化の必要性が生じる．我々は2種類の仮説検定を用いた新たな意思決定アプローチの手法を提案する．我々の手法は，二つの分布の重なり部分に属するデータや分布外データを検知することができる．また，不確実性の定量化を学習済みのモデルから得られる訓練データの特徴量の経験分布で行い，また分類を行うための閾値は各々の場面で設定する有意水準αによって決まるα分位点と1-α分位点であるため，従来に比べて計算コストが低くかつシンプルな手法となっている．スパイラルパターンの分類と医用画像において我々の手法の性能を示す．, Binary classification is a task that involves the classification of data into one of two distinct classes. It is widely utilized in various fields. However, conventional classifiers tend to make overconfident predictions for data that belong to overlapping regions of the two class distributions or for data outside the distributions (out-of-distribution data). Therefore, conventional classifiers should not be applied in high-risk fields where classification results can have significant consequences. In order to address this issue, it is necessary to quantify uncertainty and adopt decision-making approaches that take it into account. Many methods have been proposed for this purpose; however, implementing these methods often requires performing resampling, improving the structure or performance of models, and optimizing the thresholds of classifiers. We propose a new decision-making approach using two types of hypothesis testing. This method is capable of detecting ambiguous data that belong to the overlapping regions of two class distributions, as well as out-of-distribution data that are not included in the training data distribution. In addition, we quantify uncertainty using the empirical distribution of feature values derived from the training data obtained through the trained model. The classification threshold is determined by the α-quantile and (1-α)-quantile, where the significance level α is set according to each specific situation. Consequently, our method is simpler and incurs lower computational costs compared to conventional methods. We demonstrate the performance of our method on spiral patterns and medical images.},
 title = {仮説検定を用いた不確実性を含むクラス分類の手法},
 year = {2025}
}