The discovery of knowledge through data mining provides a valuable asset for addressing decision making problems. While 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, considering the importance of telemarketing for business, in particular in bank marketing.
In the same context using the same dataset REF proposed a divide and conquer strategy using neural network data mining technique in order to divide the problem into a smaller manageable sub-problems.
A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing
Expert Systems
Expert Systems
Computer Science