{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,9,17]],"date-time":"2022-09-17T06:18:41Z","timestamp":1663395521965},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,14]]},"abstract":"<jats:p>Imbalance data processing is one of big issues for machine learning. There are some proposed approaches. On the other hand, Maharanobis-Taguchi System (MTS) is a well-known approach in quality engineering. Although the target of both researches are similar, there are few researches combining these methods. In this paper, we focus on the similarity between them and propose a method to handle the imbalance data with MTS. Our proposal makes 2 prediction models, one is based on MTS and the other is based on a machine learning algorithm, from imbalanced data as following steps. First, it divides the training data into the major class, the minor class and the border class by Maharanobis distance gotten by MTS. Secondly, it makes a prediction model from the border class using a machine learning algorithm. This model is the second prediction model. In order to classify new instances, our proposed idea classifies by the first model based on MTS firstly. If it is classified in the major or minor class, the method answers this classification result, otherwise, it classifies by the second models based on a machine learning algorithm. In order to evaluate this idea, we handle some the imbalanced data by our idea and other methods and compare those results. Although the experimental result doesn\u2019t show the advantage of our idea, we get the suggestion for improving our approach.<\/jats:p>","DOI":"10.3233\/faia220298","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:34Z","timestamp":1663319314000},"source":"Crossref","is-referenced-by-count":0,"title":["A Study on an Imbalanced Data Processing Method with Maharanobis-Taguchi System"],"prefix":"10.3233","author":[{"given":"Masaki","family":"Kurematsu","sequence":"first","affiliation":[{"name":"Faculty of Software and Information Science, Iwate Prefectural University, Japan"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220298","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T09:08:35Z","timestamp":1663319315000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220298","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}