%0 Journal Article %1 Du2014474 %A Du, Gengshen %A Ruhe, Guenther %D 2014 %J Information Sciences %K release.planning %N 0 %P 474 - 489 %R http://dx.doi.org/10.1016/j.ins.2009.12.017 %T Two machine-learning techniques for mining solutions of the ReleasePlanner™ decision support system %U http://www.sciencedirect.com/science/article/pii/S0020025509005477 %V 259 %X Decision support systems (DSSs) perform complex computations to provide suggestions regarding decision-making and problem solving. Quite often, the \DSS\ solutions are not fully accepted by users because \DSSs\ work as a black box so that the users cannot fully understand where the results came from and how they were derived. Explanations of the generated \DSSs\ solutions are expected to mitigate this situation. In this paper, two machine-learning techniques, called rough set analysis (RSA) and dependency network analysis (DNA), are proposed for mining \DSS\ solutions. The mining results are provided to the users as explanations for those solutions. Two parts of research results are described. First, a framework applying \RSA\ and \DNA\ for generating explanations for \DSS\ solutions is presented. This framework is generic and applicable to many other DSSs. Second, as a proof-of-concept, the applications of \RSA\ and \DNA\ techniques are demonstrated through a case study of mining patterns from input-output pairs of ReleasePlanner™, a specific \DSS\ for product release planning. Our evaluation indicates that the explanations generated by \RSA\ and \DNA\ improve the overall user acceptance of results provided by this specific DSS.

@article{Du2014474, abstract = {Decision support systems (DSSs) perform complex computations to provide suggestions regarding decision-making and problem solving. Quite often, the \{DSS\} solutions are not fully accepted by users because \{DSSs\} work as a black box so that the users cannot fully understand where the results came from and how they were derived. Explanations of the generated \{DSSs\} solutions are expected to mitigate this situation. In this paper, two machine-learning techniques, called rough set analysis (RSA) and dependency network analysis (DNA), are proposed for mining \{DSS\} solutions. The mining results are provided to the users as explanations for those solutions. Two parts of research results are described. First, a framework applying \{RSA\} and \{DNA\} for generating explanations for \{DSS\} solutions is presented. This framework is generic and applicable to many other DSSs. Second, as a proof-of-concept, the applications of \{RSA\} and \{DNA\} techniques are demonstrated through a case study of mining patterns from input-output pairs of ReleasePlanner™, a specific \{DSS\} for product release planning. Our evaluation indicates that the explanations generated by \{RSA\} and \{DNA\} improve the overall user acceptance of results provided by this specific DSS. }, added-at = {2014-12-21T14:31:55.000+0100}, author = {Du, Gengshen and Ruhe, Guenther}, biburl = {https://www.bibsonomy.org/bibtex/27fe9d4bf92c096b2b4a298e3347b9371/samuelfricker}, doi = {http://dx.doi.org/10.1016/j.ins.2009.12.017}, interhash = {f58399f66c577c40f2123f16ab26a95c}, intrahash = {7fe9d4bf92c096b2b4a298e3347b9371}, issn = {0020-0255}, journal = {Information Sciences }, keywords = {release.planning}, number = 0, pages = {474 - 489}, timestamp = {2014-12-21T14:31:55.000+0100}, title = {Two machine-learning techniques for mining solutions of the ReleasePlanner™ decision support system }, url = {http://www.sciencedirect.com/science/article/pii/S0020025509005477}, volume = 259, year = 2014 }