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

Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo

El diseño y fabricación de hardware es costoso, tanto en tiempo como en inversión económica, razón por la que los circuitos integrados se fabrican siempre en gran volumen, para aprovechar la economía de escala. Por esa razón la mayoría de procesadores fabricados son de propósito general, ampliando así su campo de aplicaciones. En los últimos años, sin embargo, cada vez se fabrican más procesadores para aplicaciones específicas, entre ellos aquellos destinados a acelerar el trabajo con redes neuronales profundas. Este artículo introduce la necesidad de este tipo de hardware especializado, describiendo su finalidad, funcionamiento e implementaciones actuales.The design and manufacture of hardware is expensive, both in time and in economic investment, which is why integrated circuits are always manufactured in large volume, to take advantage of economies of scale. For this reason, the majority of processors manufactured are general purpose, thus expanding its range of applications. In recent years, however, more and more processors are being manufactured for specific applications, including those aimed at accelerating work with deep neural networks. This article introduces the need for this type of specialized hardware, describing its purpose, operation and current implementations.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadore

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further information. arXiv admin note: text overlap with arXiv:1812.0005

DESReg: Dynamic Ensemble Selection library for Regression tasks

open access articleNowadays, regression is a very demanded predictive task to solve a wide range of problems belonging to different research and society areas. Examples of applications include industry, economic, medical and energy fields. Ensemble methodology works by merging the output obtained from a set of base methods (learners), achieving successful results in both classification and regression tasks. Traditional ensembles use the output of the whole set of base methods, in a static way, to obtain the result of the ensemble. However, latest studies show that dynamic selection of learners or even dynamic aggregation of their outputs produce better results. Methodologies that integrate these techniques are called dynamic ensembles or dynamic ensemble selection. Although the literature and tools to work with dynamic ensembles for classification tasks is abundant, for regression tasks these resources are scarcer. This paper aims to mitigate these shortcomings by presenting a library for the design, development and execution of dynamic ensembles for regression problems. Specifically, the Python software package DESReg is presented. This library allows us to access to the latest dynamic ensemble techniques in the field, standing out for its high configurability, its support for extending it with user-defined functions or its parallel computation capabilities