کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969123 | 1449895 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Preference rules for label ranking: Mining patterns in multi-target relations
ترجمه فارسی عنوان
مقررات ترجیحی برای رتبه بندی برچسب: الگوهای معدن در روابط چندگانه
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کلمات کلیدی
رتبه بندی برچسب، قوانین انجمن، مقایسه دو طرفه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Fusion - Volume 40, March 2018, Pages 112-125
Journal: Information Fusion - Volume 40, March 2018, Pages 112-125
نویسندگان
Cláudio Rebelo de Sá, Paulo Azevedo, Carlos Soares, AlÃpio Mário Jorge, Arno Knobbe,