کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
397263 1438436 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hierarchical multilabel classification based on path evaluation
ترجمه فارسی عنوان
طبقه بندی چندبعدی سلسله مراتبی بر اساس ارزیابی مسیر
کلمات کلیدی
طبقه بندی چند لایک، طبقه بندی سلسله مراتبی، طبقه بندی های زنجیره ای
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• A novel Hierarchical Multilabel Classification algorithm for tree and DAG structures.
• It adds an extra attribute to include relations between classes.
• It incorporates a novel weighting scheme and scores all the paths.
• It incorporates a novel pruning technique for non-mandatory leaf node prediction.

Multi-label classification assigns more than one label for each instance; when the labels are ordered in a predefined structure, the task is called Hierarchical Multi-label Classification (HMC). In HMC there are global and local approaches. Global approaches treat the problem as a whole but tend to explode with large datasets. Local approaches divide the problem into local subproblems, but usually do not exploit the information of the hierarchy. This paper addresses the problem of HMC for both tree and Direct Acyclic Graph (DAG) structures whose labels do not necessarily reach a leaf node. A local classifier per parent node is trained incorporating the prediction of the parent(s) node(s) as an additional attribute to include the relations between classes. In the classification phase, the branches with low probability to occur are pruned, performing non-mandatory leaf node prediction. Our method evaluates each possible path from the root of the hierarchy, taking into account the prediction value and the level of the nodes; selecting the path (or paths in the case of DAGs) with the highest score. We tested our method with 20 datasets with tree and DAG structured hierarchies against a number of state-of-the-art methods. Our method proved to obtain superior results when dealing with deep and populated hierarchies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 68, January 2016, Pages 179–193
نویسندگان
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