Proposal of Feature Value Selection Method for Time-Critical Learning

The development of IoT has led to the creation of a data-enriched environment that enables data gathering by using distributed sensors and terminals. However, in this environment, the cost of data analysis has increased. Machine learning has gained attention for reducing the cost because enabling automatic data analysis, as well as multidimensional data, is expected. However, for enormous data, such as Big Data, we still have to pay costs. Therefore, selecting feature values when using machine learning technology is essential, especially as inputs of a classifier. Selecting the feature values increases its estimation accuracy. Moreover, the time cost, as well as calculation cost, needs consideration for the actual time-critical use of machine learning, especially in its learning process. Therefore, in this study, we proposed an algorithm that selected suitable feature values in required time. The proposed method consists of two stages: stepwise input selection stage using ANOVA and feature deletion stage according to the contribution rate of the features to estimate accuracy. These selection and deletion processes continue until the required processing time. We confirmed the efficiency of the proposed method by using an environment of a crystallization process in a factory and a household's occupancy estimation. A comparison with the original stepwise input method proved that the proposed method improved the estimation accuracy by 2
^%
and 5% in the estimation of the substance amount of the crystallization process and household's occupancy, respectively.