Article ID Journal Published Year Pages File Type
4970135 Pattern Recognition Letters 2017 9 Pages PDF
Abstract
While an incorrect identification of underlying dependency in data can lead to a flawed conclusion, recognizing legitimate dependency allows for the opportunity to adapt a model in a correct manner. In this regard, modeling the inter-dependencies in multi-label classification (multi target prediction) is one of the challenging tasks from a machine learning point of view. While common approaches seek to exploit so-called correlated information from labels, this can be improved by assuming the interactions between labels. A well-known tool to model the interaction between attributes is the Choquet integral; it enables one to model non-linear dependencies between attributes. Beyond identifying proper prior knowledge in data (if such knowledge exists), establishing suitable models that are in agreement with prior knowledge is not always a trivial task. In this paper, we propose a first step towards modeling label dependencies for multi-target classifications in terms of positive and negative interactions. In the experimental, we demonstrate real gains by applying this approach.
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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