{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T08:36:04Z","timestamp":1771576564126,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>We study the following problem: given a labeled dataset and a specific datapoint \u223cx, how did the i-th feature influence the classification for \u223cx? We identify a family of numerical influence measures \u2014 functions that, given a datapoint \u223cx, assign a numeric value \u03c6i(\u223cx) to every feature i, corresponding to how altering i\u2019s value would influence the outcome for \u223cx. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.3301718","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T22:03:24Z","timestamp":1568412204000},"page":"718-725","source":"Crossref","is-referenced-by-count":8,"title":["Axiomatic Characterization of Data-Driven Influence Measures for Classification"],"prefix":"10.1609","volume":"33","author":[{"given":"Jakub","family":"Sliwinski","sequence":"first","affiliation":[]},{"given":"Martin","family":"Strobel","sequence":"additional","affiliation":[]},{"given":"Yair","family":"Zick","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/3850\/3728","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/3850\/3728","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T07:28:13Z","timestamp":1667806093000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/3850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.3301718","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}