Applying Feature Selection Methods to Improve the Predictive Model of a Direct Marketing Problem

Tan, Ding-Wen; Sim, Yee Wai; Yeoh, William

doi:10.1007/978-3-642-22170-5_14

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2012

2022

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“…Nowadays, S. Widz Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland DSSs are used in a number of fields, such as investment [1], cost accounting [2], marketing [3], production [4], supply chain management [5] and many others [6].…”

Section: Introductionmentioning

confidence: 99%

Rough Set Based Decision Support—Models Easy to Interpret

Widz

Ślęzak

2012

Rough Sets: Selected Methods and Applications in Management and Engineering

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Rapid evolution of technology allows people to record more data than ever. Gathered information is intensively used by data analysts and domain experts. Collections of patterns extracted from data compose models (compact representations of discovered knowledge), which are at the heart of each decision support system. Models based on mathematically sophisticated methods may achieve high accuracy but they are hardly understandable by decision-makers. Models relying on symbolic, e.g. rule based methods can be less accurate but more intuitive. In both cases, feature subset selection leads to an increase of interpretability and practical usefulness of decision support systems. In this chapter, we discuss how rough sets can contribute in this respect.S. Widz ( ) XPLUS SA,

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