In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy an instance often depends on not only itself but also its context in corresponding bag. From viewpoint causal inference, such bag contextual prior works as a confounder and may result model robustness interpretability issues. Focusing this problem, we propose novel interventional (IMIL...

Fundamental and technical analyses are two common methods for predicting the future behavior of the stock. Fundamental analysis focuses on the economic forces of supply and demand which cause stock prices change. On the other hand, technical analysis examines historical data relating to changes in the price and trading volume by using graphs and indicators as a primary tool to predict future pr...

Kidney exchange programmes increase the rate of living donor kidney transplants, and operations research techniques are vital to such programmes. These techniques, as well changes policy regarding programmes, often tested using random instances created by a Saidman generator. We show that produced generator differ from real-world across number important parameters, including average recipients ...