Kazunori Iwata

OLS-regression fails to provide meaningful solutions under large numbers of predictor variables due to the presence of multicollinearity. Sparse regression, or best subset selection, is used in such cases utilizing norm-0 control or norm-1 regularization. Mixed-integer optimization models resulting under norm-0 control, however, are computationally intractable although recent advances have been made for a moderate number of predictors. This paper contributes with a new efficient approach in very large dimensions under successive separable quadratic approximation of the mean squared error (MSE) function. At every iteration, given a current pivot solution, a separable form of the MSE function is minimized over a local hypercube trust region that is discretized to obtain an all-integer optimization subproblem employing norm-0 and norm-1 parametrization. Each subproblem is solved efficiently using the entropy-based constraint surrogation technique (ISCENT). The true MSE value associated with the subproblem optima is then used to specify a target MSE with specified tolerance, and the local trust region is enumerated to identify solutions that satisfy the target. With successively shrinking local hypercubes, along with corresponding subproblem optima and target enumeration, the method terminates with a high quality sparse predictive system. We test the method using two high-dimensional applications: financial index-tracking portfolio selection using 225 assets, and cancer prediction using genomic data having 906,600 predictors representing genetic variations for a sample of 704 humans. The proposed approach is shown to be more efficient and effective relative to the standard OLS or Lasso/Ridge models in providing accurate predictions.

交通事故はさまざまな要因から発生し，その状況は多くの項目からなる統計情報として記録されている．このデータは事故対策の立案のために，要因分析に利用されるが，その結果を対策へと結びつけるためには，分析の信頼性が重要である．近年では，膨大な交通事故データから潜在的な要因を抽出するために，機械学習を用いた分析がおこなわれている．本稿では，その事例の一つとして自己組織化マップを用いた要因分析を取り上げる．自己組織化マップのような教師なし学習では，収束性や解釈性から結果の信頼性を評価する必要がある．しかし，交通事故データは多くの項目を持ち，欠損やノイズも含む．そのため，学習の収束を得るには，特徴の選択や値の類型化など，適切な前処理が必要となる．また，収束が得られたとしても結果の解釈が難しい場合がある一方で，十分に収束していなくても有用な知見が得られることもある．機械学習を用いた交通事故分析では，このように入力データの複雑さに依存した，いくらかの課題がある．本稿では，自己組織化マップを用いた事例を通じ，機械学習を用いた交通事故の要因分析において，信頼性のある結果を得るための方法について解説する．

This study not only proposes a valuable model for predicting missing words in classical Japanese literature but also suggests the potential of this model to be instrumental in repairing the literature. It could significantly advance the field of Natural Language Processing research in the context of historical literature. In recent years, Natural Language Processing has been applied to artificial intelligence programs such as ChatGPT and literary works. However, Natural Language Processing research in Japan has mainly focused on modern Japanese, and research in Japanese classical literature has yet to progress enough. Our research takes a novel approach by attempting to forecast missing words in classical Japanese literature. It creates several language models based on three pieces of classical literature using the Skip-gram of fastText. We employ LOOCV (leave-one-out cross-validation) to validate each model's accuracy. The results highlight significant differences between the modern language model and our proposed models, which we attribute to the historical context. Next, the experiment demonstrates the efficiency of our model creation method in predicting a missing word. The results of the experiments show that our proposed method can predict words similar to a missing word.

This paper describes an integrated learning system for first-year students to learn basic computer skills, including automated grading modules for typewriting and MS-Excel files and MS-Word files. The system aims to relieve teachers’ workloads to grade many MS-Excel and MS-Word files. It also provides immediate feedback and has a mechanism to prevent students from submitting copied files. In addition, this paper describes the time to grade typewriting, MS-Excel, and MS-Word files. It computes the students’ average normalized gain by using the operational records of the system in our university in 2021. The average normalized gain shows the variation between students’ computer skills decreased. These results, therefore, indicate the effectiveness of the system.