Wrapping practical problems into a machine learning framework: using water pipe failure prediction as a case study Online publication date: Mon, 24-Jul-2017
by Jianlong Zhou; Jinjun Sun; Yang Wang; Fang Chen
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 16, No. 3, 2017
Abstract: Despite the recognised value of machine learning (ML) techniques and high expectation of applying ML techniques within various applications, users often find it difficult to effectively apply ML techniques in practice because of complicated interfaces between ML algorithms and users. This paper presents a work flow of wrapping practical problems into an ML framework. The water pipe failure prediction is used as a case study to show that the applying process can be divided into various steps: obtain domain data, interview with domain experts, clean/pre-process and preview original domain data, extract ML features, set up ML models, explain ML results and make decisions, as well as make feedback to the system based on decision making. In this process, domain experts and ML developers need to collaborate closely in order to make this workflow more effective.
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