We have developed an automated Japanese short-answer scoring and support machine for new National Center written test exams. Our approach is based on the fact that accurate recognizing textual entailment and/or synonymy has been almost impossible for several years. The system generates automated scores on the basis of evaluation criteria or rubrics, and human raters revise them. The system determines semantic similarity between the model answers and the actual written answers as well as a certain degree of semantic identity and implication. Owing to the need for the scoring results to be classified at multiple levels, we use random forests to utilize many predictors effectively rather than use support vector machines. An experimental prototype operates as a web system on a Linux computer. We compared human scores with the automated scores for a case in which 3--6 allotment points were placed in 8 categories of a social studies test as a trial examination. The differences between the scores were within one point for 70--90 percent of the data when high semantic judgment was not needed.

This paper presents a revised procedure that imputes missing values by using random forests on semi-supervised data. The method has a feature that not only allows missing data to be found in a response variable but in a predictive variable, and furthermore, it can now deal with any types of data, i.e., numerical values, categories and categories with an order. By evaluating this method using Titanic data and eleven UC Irvine repository datasets, we found that our method performed fairly well, and a method of naive median imputation was also suitable in these cases.

This paper presents a procedure that imputes missing values by using random forests on semi-supervised data. We found that the rate of correct classification of our method is higher than that of other methods: a simple expansion of Liaw's "rfImpute" for (un)supervised data and the k-nearest neighbor method (kNN). Our method can handle missing predictor variables as well as missing response variable. An imputation that uses random forests for semi-supervised cases in the training data set has never been implemented until now.

We investigate van Rijsbergen's F-measure, break-even point, and 11-point averaged precision, all of which can be translated into 1-dimensional scalar quantity from the precision and the recall. These investigations can be done by comparing to tetrachoric (four-fold) correlation coefficient and phi (four-fold point) coefficient, which are often used as the index of statistical association in a 2/spl times/2 contingency table. The results show that when a fallout rate is less than 0.1, (1) the F/sub 1/ measure has similar properties of the phi coefficient, (2) the break-even point is almost equivalent to a phi coefficient, and (3) the 11-point averaged precision should be a measure, which is larger than a phi coefficient and has a value smaller than a tetrachoric correlation coefficient.