Machine Learning
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Robust Classification for Imprecise Environments
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Improving Personalization Solutions through Optimal Segmentation of Customer Bases
IEEE Transactions on Knowledge and Data Engineering
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In Brazil, power generating, transmitting and distributing companies operating in the regulated market are paid for their equipment availability. In case of system unavailability, the companies are financially penalized, more severely, on unplanned interruptions. This work presents a domain driven data mining approach for estimating the risk of systems' unavailability based on their component equipments historical data, within one of the biggest Brazilian electric sector companies. Traditional statistical estimators are combined with the concepts of Recency, Frequency and Impact (RFI) for producing variables containing behavioral information finely tuned to the application domain. The unavailability costs are embedded in the problem modeling strategy. Logistic regression models bagged via their median score achieved Max_KS=0.341 and AUC_ROC=0.699 on the out-of-time data sample. This performance is much higher than the previous approaches attempted within the company. The system has been put in operation and will be monitored for the performance reassessment and maintenance re-planning.