01025nas a2200193   4500000000100000000000100001000000100002008004100003100001600044700001600060700001700076700001600093700001900109245004800128300001400176490000700190520062000197022001400817       2013                            d1 aD.T. Nguyen1 aC.D. Nguyen1 aR. Hargraves1 aL.A. Kurgan1 aKrzysztof Cios00ami-DS: Multiple-instance learning algorithm  a143–1540 v433 aMultiple-instance learning (MIL) is a supervised learning technique that addresses the problem of classifying bags of instances instead of single instances. In this paper, we introduce a rule-based MIL algorithms, called mi-DS, and compare it with 21 existing MIL algorithms on 26 commonly used data sets. The results show that mi-DS performs on par with or better than several well-known algorithms and generates models characterized by balanced values of precision and recall. Importantly, the introduced method provides a framework that can be used for converting other rule-based algorithms into MIL algorithms.  a2168-2267