Cognitive data imputation: case study in maintenance cost estimation

Cost estimation is critical for effective decision making in engineering projects. However, it is often hampered by a lack of sufficient data. For this, data imputation techniques can be used to estimate missing costs based on statistical estimates or analogies with historical data. However, these techniques are often limited because they do not consider the existing knowledge of experts. In this paper, a novel cognitive data imputation technique is proposed for cost estimation that uses explanatory interactive machine learning to integrate and improve human knowledge. Through a case study in maintenance cost estimation the effectiveness of the approach is demonstrated.

成本估算对于工程项目中的有效决策至关重要。然而，它常常因缺乏足够数据而受阻。为此，可采用数据插补（data imputation）技术，基于统计估算或与历史数据的类比来估计缺失的成本。然而，这些技术往往存在局限性，因为它们未考虑专家已有的知识。本文提出了一种用于成本估算的新型认知数据插补技术，该技术利用解释性交互式机器学习（explanatory interactive machine learning）整合并提升人类知识。通过一项维护成本估算的案例研究，验证了该方法的有效性。