On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

Layout Inference and Table Detection in Spreadsheet Documents

Spreadsheets have found wide use in many different domains and settings. They provide a broad range of both basic and advanced functionalities. In this way, they can support data collection, transformation, analysis, and reporting. Nevertheless, at the same time spreadsheets maintain a friendly and intuitive interface. Additionally, they entail no to very low cost. Well-known spreadsheet applications, such as OpenOffice, LibreOffice, Google Sheets, and Gnumeric, are free to use. Moreover, Microsoft Excel is widely available, with millions of users worldwide. Thus, spreadsheets are not only powerful tools, but also have a very low entrance barrier. Therefore, they have become very popular with novices and professionals alike. As a result, a large volume of valuable data resides in these documents. From spreadsheets, of particular interest are data coming in tabular form, since they provide concise, factual, and to a large extend structured information. One natural progression is to transfer tabular data from spreadsheets to databases. This would allow spreadsheets to become a direct source of data for existing or new business processes. It would be easier to digest them into data warehouses and to integrate them with other sources. Nevertheless, besides databases, there are other means to work with spreadsheet data. New paradigms, like NoDB, advocate querying directly from raw documents. Going one step further, spreadsheets together with other raw documents can be stored in a sophisticated centralized repository, i.e., a data lake. From then on they can serve (on-demand) various tasks and applications. All in all, by making spreadsheet data easily accessible, we can prevent information silos, i.e., valuable knowledge being isolated and scattered in multiple spreadsheet documents. Yet, there are considerable challenges to the automatic processing and understanding of these documents. After all, spreadsheets are designed primarily for human consumption, and as such, they favor customization and visual comprehension. Data are often intermingled with formatting, formulas, layout artifacts, and textual metadata, which carry domain-specific or even user-specific information (i.e., personal preferences). Multiple tables, with different layout and structure, can be found on the same sheet. Most importantly, the structure of the tables is not known, i.e., not explicitly given by the spreadsheet documents. Altogether, spreadsheets are better described as partially structured, with a significant degree of implicit information. In literature, the automatic understanding of spreadsheet data has only been scarcely investigated, often assuming just the same uniform table layout. However, due to the manifold possibilities to structure tabular data in spreadsheets, the assumption of a uniform layout either excludes a substantial number of tables from the extraction process or leads to inaccurate results. In this thesis, we primarily address two fundamental tasks that can lead to more accurate information extraction from spreadsheet documents. Namely, we propose intuitive and effective approaches for layout analysis and table detection in spreadsheets. Nevertheless, our overall solution is designed as a processing pipeline, where specialized steps build on top of each other to discover the tabular data. One of our main objectives is to eliminate most of the assumptions from related work. Instead, we target highly diverse sheet layouts, with one or multiple tables. On the same time, we foresee the presence of textual metadata and other non-tabular data in the sheet. Furthermore, we make use of sophisticated machine learning and optimization techniques. This brings flexibility to our approach, allowing it to work even with complex or malformed tables. Moreover, this intended flexibility makes our approaches transferable to new spreadsheet datasets. Thus, we are not bounded to specific domains or settings.:1 INTRODUCTION 1.1 Motivation 1.2 Contributions 1.3 Outline 2 FOUNDATIONS AND RELATED WORK 2.1 The Evolution of Spreadsheet Documents 2.1.1 Spreadsheet User Interface and Functionalities 2.1.2 Spreadsheet File Formats 2.1.3 Spreadsheets Are Partially-Structured 2.2 Analysis and Recognition in Electronic Documents 2.2.1 A General Overview of DAR 2.2.2 DAR in Spreadsheets 2.3 Spreadsheet Research Areas 2.3.1 Layout Inference and Table Recognition 2.3.2 Unifying Databases and Spreadsheets 2.3.3 Spreadsheet Software Engineering 2.3.4 Data Wrangling Approaches 3 AN EMPIRICAL STUDY OF SPREADSHEET DOCUMENTS 3.1 Available Corpora 3.2 Creating a Gold Standard Dataset 3.2.1 Initial Selection 3.2.2 Annotation Methodology 3.3 Dataset Analysis 3.3.1 Takeaways from Business Spreadsheets 3.3.2 Comparison Between Domains 3.4 Summary and Discussion 3.4.1 Datasets for Experimental Evaluation 3.4.2 A Processing Pipeline 4 LAYOUT ANALYSIS 4.1 A Method for Layout Analysis in Spreadsheets 4.2 Feature Extraction 4.2.1 Content Features 4.2.2 Style Features 4.2.3 Font Features 4.2.4 Formula and Reference Features 4.2.5 Spatial Features 4.2.6 Geometrical Features 4.3 Cell Classification 4.3.1 Classification Datasets 4.3.2 Classifiers and Assessment Methods 4.3.3 Optimum Under-Sampling 4.3.4 Feature Selection 4.3.5 Parameter Tuning 4.3.6 Classification Evaluation 4.4 Layout Regions 4.5 Summary and Discussions 5 CLASSIFICATION POST-PROCESSING 5.1 Dataset for Post-Processing 5.2 Pattern-Based Revisions 5.2.1 Misclassification Patterns 5.2.2 Relabeling Cells 5.2.3 Evaluating the Patterns 5.3 Region-Based Revisions 5.3.1 Standardization Procedure 5.3.2 Extracting Features from Regions 5.3.3 Identifying Misclassified Regions 5.3.4 Relabeling Misclassified Regions 5.4 Summary and Discussion 6 TABLE DETECTION 6.1 A Method for Table Detection in Spreadsheets 6.2 Preliminaries 6.2.1 Introducing a Graph Model 6.2.2 Graph Partitioning for Table Detection 6.2.3 Pre-Processing for Table Detection 6.3 Rule-Based Detection 6.3.1 Remove and Conquer 6.4 Genetic-Based Detection 6.4.1 Undirected Graph 6.4.2 Header Cluster 6.4.3 Quality Metrics 6.4.4 Objective Function 6.4.5 Weight Tuning 6.4.6 Genetic Search 6.5 Experimental Evaluation 6.5.1 Testing Datasets 6.5.2 Training Datasets 6.5.3 Tuning Rounds 6.5.4 Search and Assessment 6.5.5 Evaluation Results 6.6 Summary and Discussions 7 XLINDY: A RESEARCH PROTOTYPE 7.1 Interface and Functionalities 7.1.1 Front-end Walkthrough 7.2 Implementation Details 7.2.1 Interoperability 7.2.2 Efficient Reads 7.3 Information Extraction 7.4 Summary and Discussions 8 CONCLUSION 8.1 Summary of Contributions 8.2 Directions of Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLES A ANALYSIS OF REDUCED SAMPLES B TABLE DETECTION WITH TIRS B.1 Tables in TIRS B.2 Pairing Fences with Data Regions B.3 Heuristics Framewor

Layout inference and table detection in spreadsheet document

Spreadsheet applications have evolved to be a tool of great importance for businesses, open data, and scientific communities. Using these applications, users can perform various transformations, generate new content, analyze and format data such that they are visually comprehensive. The same data can be presented in different ways, depending on the preferences and the intentions of the user. These functionalities make spreadsheets user-friendly, but not as much machine-friendly. When it comes to integrating with other sources, the free-for-all nature of spreadsheets is disadvantageous. It is rather difficult to algorithmically infer the structure of the data when they are intermingled with formatting, formulas, layout artifacts, and textual metadata. Therefore, user involvement is often required, which results in cumbersome and time-consuming tasks. Overall, the lack of automatic processing methods limits our ability to explore and reuse a great amount of rich data stored into partially-structured documents such as spreadsheets. In this thesis, we tackle this open challenge, which so far has been scarcely investigated in literature. Specifically, we are interested in extracting tabular data from spreadsheets, since they hold concise, factual, and to a large extend structured information. It is easier to process such information, in order to make it available to other applications. For instance, spreadsheet (tabular) data can be loaded into databases. Thus, these data would become instantly available to existing or new business processes. Furthermore, we can eliminate the risk of losing valuable company knowledge, by moving data or integrating spreadsheets with other more sophisticated information management systems. To achieve the aforementioned objectives and advancements, in this thesis, we develop a spreadsheet processing pipeline. The requirements for this pipeline were derived from a large scale empirical analysis of real-world spreadsheets, from business and Web settings. Specifically, we propose a series of specialized steps that build on top of each other with the goal of discovering the structure of data in spreadsheet documents. Our approach is bottom-up, as it starts from the smallest unit (i.e., the cell) to ultimately arrive at the individual tables of the sheet. Additionally, this thesis makes use of sophisticated machine learning and optimization techniques. In particular, we apply these techniques for layout analysis and table detection in spreadsheets. We target highly diverse sheet layouts, with one or multiple tables and arbitrary arrangement of contents. Moreover, we foresee the presence of textual metadata and other non-tabular data in the sheet. Furthermore, we work even with problematic tables (e.g., containing empty rows/columns and missing values). Finally, we bring flexibility to our approach. This not only allows us to tackle the above-mentioned challenges but also to reuse our solution for different (spreadsheet) datasets.Els fulls de càlcul s’empren massivament en molts dominis i contexts diferents, ja que proporcionen una àmplia gamma de funcionalitats, bàsiques i avançades, de gestió de dades. D’aquesta manera, donen suport a la recollida, transformació, anàlisi i visualització de dades. A la mateixa vegada, els fulls de càlcul tenen una interfície amigable i intuïtiva i tenen un cost molt baix d’implantació. Aplicacions de full de càlcul molt conegudes, com OpenOffice, LibreOffice, Google Sheets i Gnumeric, poden utilitzar-se de forma gratuïta i d’altres, com Microsoft Excel, són a l’abast d’una gran majoria d’usuaris. Per tant, han esdevingut molt populars tant per a novells com per professionals. Com a resultat, un gran volum de dades valuoses resideixen en aquests documents. Són de particular interès les dades que es presenten en format tabular dins dels fulls de càlcul, ja que proporcionen informació concreta, factual i parcialment estructurada. Com a conseqüència, hi ha interès en transferir dades tabulars des de fulls de càlcul a bases de dades. Això permetria que els fulls de càlcul es converteixin en una font directa de dades per a processos empresarials, i introduir aquestes dades als magatzems de dades i integrar-les amb altres fonts. Un pas més enllà, els fulls de càlcul juntament amb altres documents en brut es poden emmagatzemar en repositoris de dades centralitzats avançats, com per exemple, els data lake. Un cop al data lake, es podran fer servir (sota demanda) per a diverses tasques i aplicacions. Tot plegat, l’objectiu és fer accessibles les dades emmagatzemades als fulls de càlcul. Malgrat tot, hi ha reptes considerables en el processament i comprensió automàtica d’aquests documents. Els fulls de càlcul estan dissenyats principalment per al consum humà i, per tant, afavoreixen la personalització i la comprensió visual. Les dades sovint s’entrellacen amb formatació, fórmules, artefactes de disseny i metadades textuals, que porten informació específica del domini o fins i tot informació específica de l’usuari. Al mateix full es poden trobar diverses taules, amb una estructura i disseny diferents. A més, el format de cada taula no es declara a priori, és a dir, no hi ha cap mecanisme per definir l’estructura d’una taula, com passa a les bases de dades. Per aquest motiu, els fulls de càlcul es coneixen com a fonts de dades parcialment estructurades, amb un grau rellevant d'informació implícita. A la literatura, la comprensió automàtica de les dades emmagatzemades en fulls de càlcul s'ha investigat superficialment, sovint assumint el mateix format uniforme de taula a tots els fulls de càlcul. Tanmateix, a causa de les múltiples possibilitats d'estructurar les dades tabulars en fulls de càlcul, la suposició d'un disseny uniforme o bé exclou un nombre substancial de taules del procés d'extracció o condueix a resultats inexactes. En aquesta tesi, abordem tasques fonamentals que contribueixen a l’extracció d’informació dels fulls de càlcul d’una manera més precisa. Proposem mètodes intuïtius i eficaços per a l’anàlisi de la distribució i detecció de taules en fulls de càlcul. Un dels nostres objectius principals és eliminar la majoria dels supòsits de l’estat de l’art actual. Per fer-ho, considerem estructures tabulars altament heterogènies, contingudes en fulls de càlcul amb una o més taules. Addicionalment, preveiem la presencia de metadades i altres tipus de dades no tabulars al mateix full. Per últim, utilitzem tècniques d’optimització i d’aprenentatge automàtic per identificar l’estructura de les taules. Això aporta flexibilitat al nostre enfocament, permetent-lo treballar, fins i tot, amb taules complexes o malformades. Aquesta flexibilitat fa que els nostres mètodes siguin transferibles a nous conjunts de fulls de càlcul amb dades d’altres dominis. Per tant, no estem limitats a dominis o configuracion

Layout inference and table detection in spreadsheet document

Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Technische Universität DresdenSpreadsheet applications have evolved to be a tool of great importance for businesses, open data, and scientific communities. Using these applications, users can perform various transformations, generate new content, analyze and format data such that they are visually comprehensive. The same data can be presented in different ways, depending on the preferences and the intentions of the user. These functionalities make spreadsheets user-friendly, but not as much machine-friendly. When it comes to integrating with other sources, the free-for-all nature of spreadsheets is disadvantageous. It is rather difficult to algorithmically infer the structure of the data when they are intermingled with formatting, formulas, layout artifacts, and textual metadata. Therefore, user involvement is often required, which results in cumbersome and time-consuming tasks. Overall, the lack of automatic processing methods limits our ability to explore and reuse a great amount of rich data stored into partially-structured documents such as spreadsheets. In this thesis, we tackle this open challenge, which so far has been scarcely investigated in literature. Specifically, we are interested in extracting tabular data from spreadsheets, since they hold concise, factual, and to a large extend structured information. It is easier to process such information, in order to make it available to other applications. For instance, spreadsheet (tabular) data can be loaded into databases. Thus, these data would become instantly available to existing or new business processes. Furthermore, we can eliminate the risk of losing valuable company knowledge, by moving data or integrating spreadsheets with other more sophisticated information management systems. To achieve the aforementioned objectives and advancements, in this thesis, we develop a spreadsheet processing pipeline. The requirements for this pipeline were derived from a large scale empirical analysis of real-world spreadsheets, from business and Web settings. Specifically, we propose a series of specialized steps that build on top of each other with the goal of discovering the structure of data in spreadsheet documents. Our approach is bottom-up, as it starts from the smallest unit (i.e., the cell) to ultimately arrive at the individual tables of the sheet. Additionally, this thesis makes use of sophisticated machine learning and optimization techniques. In particular, we apply these techniques for layout analysis and table detection in spreadsheets. We target highly diverse sheet layouts, with one or multiple tables and arbitrary arrangement of contents. Moreover, we foresee the presence of textual metadata and other non-tabular data in the sheet. Furthermore, we work even with problematic tables (e.g., containing empty rows/columns and missing values). Finally, we bring flexibility to our approach. This not only allows us to tackle the above-mentioned challenges but also to reuse our solution for different (spreadsheet) datasets.Els fulls de càlcul s’empren massivament en molts dominis i contexts diferents, ja que proporcionen una àmplia gamma de funcionalitats, bàsiques i avançades, de gestió de dades. D’aquesta manera, donen suport a la recollida, transformació, anàlisi i visualització de dades. A la mateixa vegada, els fulls de càlcul tenen una interfície amigable i intuïtiva i tenen un cost molt baix d’implantació. Aplicacions de full de càlcul molt conegudes, com OpenOffice, LibreOffice, Google Sheets i Gnumeric, poden utilitzar-se de forma gratuïta i d’altres, com Microsoft Excel, són a l’abast d’una gran majoria d’usuaris. Per tant, han esdevingut molt populars tant per a novells com per professionals. Com a resultat, un gran volum de dades valuoses resideixen en aquests documents. Són de particular interès les dades que es presenten en format tabular dins dels fulls de càlcul, ja que proporcionen informació concreta, factual i parcialment estructurada. Com a conseqüència, hi ha interès en transferir dades tabulars des de fulls de càlcul a bases de dades. Això permetria que els fulls de càlcul es converteixin en una font directa de dades per a processos empresarials, i introduir aquestes dades als magatzems de dades i integrar-les amb altres fonts. Un pas més enllà, els fulls de càlcul juntament amb altres documents en brut es poden emmagatzemar en repositoris de dades centralitzats avançats, com per exemple, els data lake. Un cop al data lake, es podran fer servir (sota demanda) per a diverses tasques i aplicacions. Tot plegat, l’objectiu és fer accessibles les dades emmagatzemades als fulls de càlcul. Malgrat tot, hi ha reptes considerables en el processament i comprensió automàtica d’aquests documents. Els fulls de càlcul estan dissenyats principalment per al consum humà i, per tant, afavoreixen la personalització i la comprensió visual. Les dades sovint s’entrellacen amb formatació, fórmules, artefactes de disseny i metadades textuals, que porten informació específica del domini o fins i tot informació específica de l’usuari. Al mateix full es poden trobar diverses taules, amb una estructura i disseny diferents. A més, el format de cada taula no es declara a priori, és a dir, no hi ha cap mecanisme per definir l’estructura d’una taula, com passa a les bases de dades. Per aquest motiu, els fulls de càlcul es coneixen com a fonts de dades parcialment estructurades, amb un grau rellevant d'informació implícita. A la literatura, la comprensió automàtica de les dades emmagatzemades en fulls de càlcul s'ha investigat superficialment, sovint assumint el mateix format uniforme de taula a tots els fulls de càlcul. Tanmateix, a causa de les múltiples possibilitats d'estructurar les dades tabulars en fulls de càlcul, la suposició d'un disseny uniforme o bé exclou un nombre substancial de taules del procés d'extracció o condueix a resultats inexactes. En aquesta tesi, abordem tasques fonamentals que contribueixen a l’extracció d’informació dels fulls de càlcul d’una manera més precisa. Proposem mètodes intuïtius i eficaços per a l’anàlisi de la distribució i detecció de taules en fulls de càlcul. Un dels nostres objectius principals és eliminar la majoria dels supòsits de l’estat de l’art actual. Per fer-ho, considerem estructures tabulars altament heterogènies, contingudes en fulls de càlcul amb una o més taules. Addicionalment, preveiem la presencia de metadades i altres tipus de dades no tabulars al mateix full. Per últim, utilitzem tècniques d’optimització i d’aprenentatge automàtic per identificar l’estructura de les taules. Això aporta flexibilitat al nostre enfocament, permetent-lo treballar, fins i tot, amb taules complexes o malformades. Aquesta flexibilitat fa que els nostres mètodes siguin transferibles a nous conjunts de fulls de càlcul amb dades d’altres dominis. Per tant, no estem limitats a dominis o configuracionsPostprint (published version

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features

Distributed multi-label learning on Apache Spark

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art