Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Consumer perceptions of co-branding alliances: Organizational dissimilarity signals and brand fit

This study explores how consumers evaluate co-branding alliances between dissimilar partner firms. Customers are well aware that different firms are behind a co-branded product and observe the partner firms’ characteristics. Drawing on signaling theory, we assert that consumers use organizational characteristics as signals in their assessment of brand fit and for their purchasing decisions. Some organizational signals are beyond the control of the co-branding partners or at least they cannot alter them on short notice. We use a quasi-experimental design and test how co-branding partner dissimilarity affects brand fit perception. The results show that co-branding partner dissimilarity in terms of firm size, industry scope, and country-of-origin image negatively affects brand fit perception. Firm age dissimilarity does not exert significant influence. Because brand fit generally fosters a benevolent consumer attitude towards a co-branding alliance, the findings suggest that high partner dissimilarity may reduce overall co-branding alliance performance

Resonant Raman scattering in a zero-gap semiconductor: Interference effects and deformation potentials at the E1_1 and E1_1 + Δ1\Delta_1 gaps of HgTe

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