{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:41:35Z","timestamp":1651797695976},"reference-count":15,"publisher":"IGI Global","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,10]]},"abstract":"<jats:p>Understanding diseases and human activities, and constructing highly accurate classifiers are two important tasks in bio-medicine, healthcare, and wearable sensor technology. Being able to mine high-quality patterns is useful here, as such patterns can help improve understanding and build accurate classifiers. However, most pattern mining algorithms only operate on discrete data; applying them often requires a binning step to discretize continuous attributes. This article presents a new discretization technique, called Class Distribution Curve based Binning (CDC Binning); the main idea is to use a so-called class distribution curve, which measures the class purity in sliding windows over an attribute's range, to construct binning intervals. Experiments show that (1) CDC Binning outperforms existing binning methods in discovering high-quality patterns, especially when the class distribution curve is complicated (e.g. when the two classes are two fairly similar human activities), and (2) it can outperform other binning methods by 10% in classification accuracy when using discovered patterns as features. CDC Binning is particularly useful for applications where the classes\/activities to be distinguished are similar to each other. This is especially important in wearable sensor technology where detection of behavioral or activity changes in a person (e.g. fall detection) could indicate a significant medical event.<\/jats:p>","DOI":"10.4018\/ijmstr.2017100102","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T09:10:37Z","timestamp":1524561037000},"page":"23-37","source":"Crossref","is-referenced-by-count":1,"title":["Class Distribution Curve Based Discretization With Application to Wearable Sensors and Medical Monitoring"],"prefix":"10.4018","volume":"5","author":[{"given":"Nicholas","family":"Skapura","sequence":"first","affiliation":[{"name":"Wright State University, Dayton, USA"}]},{"given":"Guozhu","family":"Dong","sequence":"additional","affiliation":[{"name":"Wright State University, Dayton, USA"}]}],"member":"2432","reference":[{"key":"IJMSTR.2017100102-0","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-011-0415-z"},{"key":"IJMSTR.2017100102-1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"IJMSTR.2017100102-2","author":"G.Dong","year":"2013","journal-title":"Contrast data mining: Concepts, algorithms, and applications"},{"key":"IJMSTR.2017100102-3","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312191"},{"key":"IJMSTR.2017100102-4","first-page":"1022","article-title":"Multi- Interval Discretization of Continuous- Valued Attributes for Classification Learning.","author":"U. M.Fayyad","year":"1993","journal-title":"Machine Learning"},{"key":"IJMSTR.2017100102-5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.166"},{"key":"IJMSTR.2017100102-6","doi-asserted-by":"publisher","DOI":"10.1023\/B:DAMI.0000005258.31418.83"},{"key":"IJMSTR.2017100102-7","doi-asserted-by":"publisher","DOI":"10.1109\/TAAI.2010.62"},{"key":"IJMSTR.2017100102-8","unstructured":"Kutzler, K. (2010). Machine learning data set repository. Retrieved from http:\/\/mldata.org"},{"key":"IJMSTR.2017100102-9","unstructured":"Lichman, M. (2013). UCI machine learning repository. Retrieved from http:\/\/archive.ics.uci.edu\/ml"},{"key":"IJMSTR.2017100102-10","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0380-z"},{"key":"IJMSTR.2017100102-11","doi-asserted-by":"crossref","unstructured":"Rechy-Ramirez, F., Mesa, H. G., Mezura-Montes, E., & Cruz-Ramirez, N. (2011). Times series discretization using evolutionary programming. In Advances in Soft Computing (pp. 225-234).","DOI":"10.1007\/978-3-642-25330-0_20"},{"key":"IJMSTR.2017100102-12","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"IJMSTR.2017100102-13","doi-asserted-by":"crossref","unstructured":"Skapura, N., & Dong, G. (2015). Distribution skew-based binning: Towards mining highly discriminative patterns from EEG\/EMG time series. In IEEE 15th International Conference on Bioinformatics and Bioengineering.","DOI":"10.1109\/BIBE.2015.7367635"},{"key":"IJMSTR.2017100102-14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"}],"container-title":["International Journal of Monitoring and Surveillance Technologies Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=204943","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:21:13Z","timestamp":1651796473000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJMSTR.2017100102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2017,10]]},"references-count":15,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.4018\/ijmstr.2017100102","relation":{},"ISSN":["2166-7241","2166-725X"],"issn-type":[{"value":"2166-7241","type":"print"},{"value":"2166-725X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10]]}}}