Abstract
AbstractThe discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. Although a list of features may characterize a problem, it is often the case that a subset of those features may influence more a certain group of events constituting a sub‐problem within the original problem. We propose a divide‐and‐conquer strategy for data mining using both the data‐based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits. As a result, the call direction (inbound/outbound) was considered the most suitable candidate feature. The inbound telemarketing sub‐problem re‐evaluation led to a large increase in targeting performance, confirming the benefits of such approach and considering the importance of telemarketing for business, in particular in bank marketing.
Highlights
Data mining (DM) enables to unveil previously undiscovered knowledge, providing leverage for decision making (Witten et al, 2016)
We propose a divide and conquer strategy for data mining using both the data-based sensitivity analysis for extracting feature relevance and expert evaluation for splitting the problem of characterizing telemarketing contacts to sell bank deposits
Recent developments continue to use divide and conquer strategies for improving DM modelling tasks, such as: improving the performance of support vector machines (Hsieh et al, 2014); and handling large datasets by using an hierarchical classification for dividing the problem in smaller fractions which can be dealt with neural networks (Fritsch & Finke, 2012)
Summary
Data mining (DM) enables to unveil previously undiscovered knowledge, providing leverage for decision making (Witten et al, 2016). A bank telemarketing dataset was enriched with social and economic context features, leading to a tuned model that enabled to reach 79% of the deposit subscribers by selecting the half better classified clients (Moro et al, 2014). Such a model was improved by including customer lifetime value related features, increasing the performance to 83% of subscribers with the half better classified contacts (Moro et al, 2015b).
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