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- W69383807 abstract "Like model selection in statistics, the choice of appropriate Data Mining Algorithms (DM-Algorithms) is a very important task in the process of Knowledge Discovery. Due to this fact it is necessary to have sophisticated metrics that can be used as comparators to evaluate alternative DM-algorithms. It has been shown in literature, that Data Envelopment Analysis (DEA) is an appropriate platform to develop multi-criteria evaluation metrics that can consider -in contrary to mono-criteria metrics — all positive and negative properties of DM-algorithms.We discuss different extensions of DEA that enable consideration of qualitative properties of DM-algorithms and consideration of users preferences in development of evaluation metrics. The results open new discussions in the general debate on model selection in statistics and machine learning." @default.
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- W69383807 date "1998-08-27" @default.
- W69383807 modified "2023-09-23" @default.
- W69383807 title "Towards the personalization of algorithms evaluation in Data Mining" @default.
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