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

Integration of Heterogeneous Databases: Discovery of Meta-Information and Maintenance of Schema-Restructuring Views

In today\u27s networked world, information is widely distributed across many independent databases in heterogeneous formats. Integrating such information is a difficult task and has been adressed by several projects. However, previous integration solutions, such as the EVE-Project, have several shortcomings. Database contents and structure change frequently, and users often have incomplete information about the data content and structure of the databases they use. When information from several such insufficiently described sources is to be extracted and integrated, two problems have to be solved: How can we discover the structure and contents of and interrelationships among unknown databases, and how can we provide durable integration views over several such databases? In this dissertation, we have developed solutions for those key problems in information integration. The first part of the dissertation addresses the fact that knowledge about the interrelationships between databases is essential for any attempt at solving the information integration problem. We are presenting an algorithm called FIND2 based on the clique-finding problem in graphs and k-uniform hypergraphs to discover redundancy relationships between two relations. Furthermore, the algorithm is enhanced by heuristics that significantly reduce the search space when necessary. Extensive experimental studies on the algorithm both with and without heuristics illustrate its effectiveness on a variety of real-world data sets. The second part of the dissertation addresses the durable view problem and presents the first algorithm for incremental view maintenance in schema-restructuring views. Such views are essential for the integration of heterogeneous databases. They are typically defined in schema-restructuring query languages like SchemaSQL, which can transform schema into data and vice versa, making traditional view maintenance based on differential queries impossible. Based on an existing algebra for SchemaSQL, we present an update propagation algorithm that propagates updates along the query algebra tree and prove its correctness. We also propose optimizations on our algorithm and present experimental results showing its benefits over view recomputation

Efficient Discovery of Ontology Functional Dependencies

Poor data quality has become a pervasive issue due to the increasing complexity and size of modern datasets. Constraint based data cleaning techniques rely on integrity constraints as a benchmark to identify and correct errors. Data values that do not satisfy the given set of constraints are flagged as dirty, and data updates are made to re-align the data and the constraints. However, many errors often require user input to resolve due to domain expertise defining specific terminology and relationships. For example, in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be captured in a pharmaceutical ontology. While functional dependencies (FDs) have traditionally been used in existing data cleaning solutions to model syntactic equivalence, they are not able to model broader relationships (e.g., is-a) defined by an ontology. In this paper, we take a first step towards extending the set of data quality constraints used in data cleaning by defining and discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out theoretical and practical foundations for OFDs, including a set of sound and complete axioms, and a linear inference procedure. We then develop effective algorithms for discovering OFDs, and a set of optimizations that efficiently prune the search space. Our experimental evaluation using real data show the scalability and accuracy of our algorithms.Comment: 12 page

Approaching new migration through Elias' 'established' and 'outsiders' lens

When considering social positions and features that become distinguishing for migrants’ positioning, scholars quite often rely on empirical descriptions, based on discrete and supposedly clearly definable factors. Whereas elements such as legal position, citizenship, etc. are of huge relevance in numerous contexts, in other domains relying on such delineations while studying discriminatory processes oversimplifies the picture. In this paper, a conceptual issue regarding the understandings of the positions of migrants (particularly recent migrations to the Western Europe) is raised. After a discussion of the definitions of ‘new migrations’, a broad heuristic device for thinking about ‘new’ migrants’ positioning will be outlined. This framework – inspired by Elias’ and Scotson’s ‘The Established and the Outsiders’ (1994 [1965]) – can be adapted in different manners by academics addressing topics related to definition, marginalization, and discriminatory processes. The central point is that although various characteristics (e.g. ethnicity, legal position) can be assigned importance in human figurations, the relationships of othering, inequality, and domination need to be seen in the light of the configuration of social relationships and power imbalances

Profiling relational data: a survey

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

Exploiting natural language structures in software informal documentation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Communication means, such as issue trackers, mailing lists, Q&A forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed DECA (Development Emails Content Analyzer), a tool based on Natural Language Parsing that classifies with high precision development emails' fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with a study involving Master's and Ph.D. students, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named NEON (Nlp-based softwarE dOcumentation aNalyzer), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns' identification and rules' definition processes, allowing a savings of more than 70% of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall

Ramex-Forum: a tool for displaying and analysing complex sequential patterns of financial products

Financial data provides a valuable up‐to‐date knowledge of the world economy. However, it is presented in extremely large data volumes, in diverse formats, and is constantly being updated at a high speed. The Ramex‐Forum algorithm is oriented to guide financial experts in finding new and relevant information.We present a sensitivity analysis and newvisualizations using an improved version of the Ramex‐Forum algorithm. The proposed algorithm is applied to two case studies – the petroleum production chain and the European financial institutions risk analysis. Different combinations of parameters and new ways to visualize data are used. Results highlight the importance of Ramex‐Forum for analysing relevant relationships in price variations in financial markets.info:eu-repo/semantics/publishedVersio