Iterative Dichotomiser Decision Tree for Risk Analysis in Innovation Management

Iterative Dichotomiser Decision Tree for Risk Analysis in Innovation Management

Hassan Arabshahi (Mazandaran University of Science and Technology, Babol, Iran) and Hamed Fazlollahtabar (Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
Copyright: © 2016 |Pages: 11
DOI: 10.4018/IJRCM.2016100102
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Abstract

This paper proposes a three phase framework for classifying innovative activities based on the corresponding calculated risk. In the developed model, the innovative activities are collected from the literature and are classified into risk classes using decision tree and Iterative Dichotomiser 3 (ID3) algorithm. The resulting rules of the tree can be proper information resources for innovators, investors and predictors of the innovation and related sector to take the best decision about their innovation, investment and risk management.
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2. Research Methodology

The framework is organized in three phases. In preliminary phase, the input data for classification is collected from historical literature. Each innovative activity is characterized by a tuple (x, y) where X is the attribute set and y is a special attribute, designated as the class label or target attribute. In this phase, database is prepared for next phase. In second phase, decision tree is drawn based on target attribute that is “risk class” here using ID3 algorithm. In the last phase, some proper rules are discovered from the tree and are explained for providing appropriate information for predictors, investors and innovators. Following, these triple phases are explained in detail.

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