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This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.<\/p>","DOI":"10.4018\/ijsi.2017100102","type":"journal-article","created":{"date-parts":[[2017,8,1]],"date-time":"2017-08-01T03:58:06Z","timestamp":1501559886000},"page":"19-32","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Classification to Effort Estimation for Embedded Software Development Projects"],"prefix":"10.4018","volume":"5","author":[{"given":"Kazunori","family":"Iwata","sequence":"first","affiliation":[{"name":"Department of Business Administration, Aichi University, Nagoya, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toyoshiro","family":"Nakashima","sequence":"additional","affiliation":[{"name":"Department of Culture-Information Studies, Sugiyama Jogakuen University, Nagoya, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshiyuki","family":"Anan","sequence":"additional","affiliation":[{"name":"Process Innovation H.Q, Omron Software Co., Ltd., Kyoto, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naohiro","family":"Ishii","sequence":"additional","affiliation":[{"name":"Department of Information Science, Aichi Institute of Technology, Nagoya, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSI.2017100102-0","unstructured":"Akaike, H. 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