Francisco Herrera Livres

This book constitutes the refereed proceedings of the 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, held in Granada, Spain, in October 2018. The 36 full papers presented were carefully selected from 240 submissions. The Conference of the Spanish Association of Artificial Intelligence (CAEPIA) is a biennial forum open to researchers from all over the world to present and discuss their latest scientific and technological advances in Antificial Intelligence (AI). Authors are kindly requested to submit unpublished original papers describing relevant research on AI issues from all points of view: formal, methodological, technical or applied.

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

This book offers a comprehensive overview of multiple instance learning (MIL), outlining its framework and key paradigms. The authors delve into essential MIL algorithms, including classification, regression, and clustering, with a particular emphasis on classification. A taxonomy is established, highlighting significant proposals and developing efficient algorithms to extract relevant information amidst uncertainty. Key applications are presented, along with an exploration of related fields such as distance metrics and alternative hypotheses. Chapters address emerging aspects of MIL, including data reduction for multi-instance issues and the challenge of imbalanced MIL data, defined at the bag level. This representation leverages ambiguity since bag labels are known, but individual instance labels remain undefined. The book also investigates multiple instance multiple label learning, which introduces flexibility and ambiguity in object representation, allowing a single object to be represented by a bag of instances with multiple associated class labels. This resource is ideal for developers and engineers applying MIL techniques to tackle real-world challenges, and it serves as a valuable reference for researchers and students seeking an in-depth understanding of MIL literature, methods, and tools.

Soft Computing is concerned with modes of computing in which precision is treated for tractability, robustness and ease of implementation, and it contains Fuzzy Sets and Genetic Algorithms among its components. Each of them have different advantages to deal with nonlinearity or explicit knowledge expression, but learning capability, as well as global and local search approaches provided by Genetic Algorithms are remarkable. This book will be revealing for all those interested in new developments and practical applications in the interface between Soft Computing and Genetic Algorithms.