Multilabel Classification : Problem Analysis, Metrics and Techniques
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 w...
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| Main Authors | , , , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cham
Springer
2016
Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319411101 3319411101 |
| DOI | 10.1007/978-3-319-41111-8 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Herrera, Francisco del Jesus, María J. Rivera, Antonio J. Charte, Francisco |
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| DOI | 10.1007/978-3-319-41111-8 |
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| Notes | Includes bibliographical references Other authors: Francisco Charte, Antonio J. Rivera, María J. del Jesus |
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| Snippet | 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... |
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| SubjectTerms | Artificial Intelligence Computer Science Data Mining and Knowledge Discovery |
| TableOfContents | 4.3 Binary Classification Based Methods -- 4.3.1 OVO Versus OVA Approaches -- 4.3.2 Ensembles of Binary Classifiers -- 4.4 Multiclass Classification-Based Methods -- 4.4.1 Labelsets and Pruned Labesets -- 4.4.2 Ensembles of Multiclass Classifiers -- 4.5 Data Transformation Methods in Practice -- 4.5.1 Experimental Configuration -- 4.5.2 Classification Results -- 4.6 Summarizing Comments -- References -- 5 Adaptation-Based Classifiers -- 5.1 Overview -- 5.2 Tree-Based Methods -- 5.2.1 Multilabel C4.5, ML-C4.5 -- 5.2.2 Multilabel Alternate Decision Trees, ADTBoost.MH -- 5.2.3 Other Tree-Based Proposals -- 5.3 Neuronal Network-Based Methods -- 5.3.1 Multilabel Back-Propagation, BP-MLL -- 5.3.2 Multilabel Radial Basis Function Network, ML-RBF -- 5.3.3 Canonical Correlation Analysis and Extreme Learning Machine, CCA-ELM -- 5.4 Vector Support Machine-Based Methods -- 5.4.1 MODEL-x -- 5.4.2 Multilabel SVMs Based on Ranking, Rank-SVM and SCRank-SVM -- 5.5 Instance-Based Methods -- 5.5.1 Multilabel kNN, ML-kNN -- 5.5.2 Instance-Based and Logistic Regression, IBLR-ML -- 5.5.3 Other Instance-Based Classifiers -- 5.6 Probabilistic Methods -- 5.6.1 Collectible Multilabel Classifiers, CML and CMLF -- 5.6.2 Probabilistic Generic Models, PMM1 and PMM2 -- 5.6.3 Probabilistic Classifier Chains, PCC -- 5.6.4 Bayesian and Tree Naïve Bayes Classifier Chains, BCC and TNBCC -- 5.6.5 Conditional Restricted Boltzmann Machines, CRBM -- 5.7 Other MLC Adaptation-Based Methods -- 5.8 Adapted Methods in Practice -- 5.8.1 Experimental Configuration -- 5.8.2 Classification Results -- 5.9 Summarizing Comments -- References -- 6 Ensemble-Based Classifiers -- 6.1 Introduction -- 6.2 Ensembles of Binary Classifiers -- 6.2.1 Ensemble of Classifier Chains, ECC -- 6.2.2 Ranking by Pairwise Comparison, RPC -- 6.2.3 Calibrated Label Ranking, CLR -- 6.3 Ensembles of Multiclass Classifiers 6.3.1 Ensemble of Pruned Sets, EPS -- 6.3.2 Random k-Labelsets, RAkEL -- 6.3.3 Hierarchy of Multilabel Classifiers, HOMER -- 6.4 Other Ensembles -- 6.5 Ensemble Methods in Practice -- 6.5.1 Experimental Configuration -- 6.5.2 Classification Results -- 6.5.3 Training and Testing Times -- 6.6 Summarizing Comments -- References -- 7 Dimensionality Reduction -- 7.1 Overview -- 7.1.1 High-Dimensional Input Space -- 7.1.2 High-Dimensional Output Space -- 7.2 Feature Space Reduction -- 7.2.1 Feature Engineering Approaches -- 7.2.2 Multilabel Supervised Feature Selection -- 7.2.3 Experimentation -- 7.3 Label Space Reduction -- 7.3.1 Sparseness and Dependencies Among Labels -- 7.3.2 Proposals for Reducing Label Space Dimensionality -- 7.3.3 Experimentation -- 7.4 Summarizing Comments -- References -- 8 Imbalance in Multilabel Datasets -- 8.1 Introduction -- 8.2 Imbalanced MLD Specificities -- 8.2.1 How to Measure the Imbalance Level -- 8.2.2 Concurrence Among Imbalanced Labels -- 8.3 Facing Imbalanced Multilabel Classification -- 8.3.1 Classifier Adaptation -- 8.3.2 Resampling Techniques -- 8.3.3 The Ensemble Approach -- 8.4 Multilabel Imbalanced Learning in Practice -- 8.4.1 Experimental Configuration -- 8.4.2 Classification Results -- 8.5 Summarizing Comments -- References -- 9 Multilabel Software -- 9.1 Overview -- 9.2 Working with Multilabel Data -- 9.2.1 Multilabel Data File Formats -- 9.2.2 Multilabel Data Repositories -- 9.2.3 The mldr.datasets Package -- 9.2.4 Generating Synthetic MLDs -- 9.3 Exploratory Analysis of MLDs -- 9.3.1 MEKA -- 9.3.2 The mldr Package -- 9.4 Conducting Multilabel Experiments -- 9.4.1 MEKA -- 9.4.2 MULAN -- 9.4.3 The RunMLClassifier Utility -- 9.5 Summarizing Comments -- References -- Glossary Intro -- Preface -- Contents -- Acronyms -- 1 Introduction -- 1.1 Overview -- 1.2 The Knowledge Discovery in Databases Process -- 1.3 Data Preprocessing -- 1.4 Data Mining -- 1.4.1 DM Methods Attending to Available Data -- 1.4.2 DM Methods Attending to Target Objective -- 1.4.3 DM Methods Attending to Knowledge Representation -- 1.5 Classification -- 1.5.1 Binary Classification -- 1.5.2 Multiclass Classification -- 1.5.3 Multilabel Classification -- 1.5.4 Multidimensional Classification -- 1.5.5 Multiple Instance Learning -- References -- 2 Multilabel Classification -- 2.1 Introduction -- 2.2 Problem Formal Definition -- 2.2.1 Definitions -- 2.2.2 Symbols -- 2.2.3 Terminology -- 2.3 Applications of Multilabel Classification -- 2.3.1 Text Categorization -- 2.3.2 Labeling of Multimedia Resources -- 2.3.3 Genetics/Biology -- 2.3.4 Other Application Fields -- 2.3.5 MLDs Repositories -- 2.4 Learning from Multilabel Data -- 2.4.1 The Data Transformation Approach -- 2.4.2 The Method Adaptation Approach -- 2.4.3 Ensembles of Classifiers -- 2.4.4 Label Correlation Information -- 2.4.5 High Dimensionality -- 2.4.6 Label Imbalance -- 2.5 Multilabel Data Tools -- References -- 3 Case Studies and Metrics -- 3.1 Overview -- 3.2 Case Studies -- 3.2.1 Text Categorization -- 3.2.2 Labeling of Multimedia Resources -- 3.2.3 Genetics/Biology -- 3.2.4 Synthetic MLDs -- 3.3 MLD Characteristics -- 3.3.1 Basic Metrics -- 3.3.2 Imbalance Metrics -- 3.3.3 Other Metrics -- 3.3.4 Summary of Characterization Metrics -- 3.4 Multilabel Classification by Example -- 3.4.1 The ML-kNN Algorithm -- 3.4.2 Experimental Configuration and Results -- 3.5 Assessing Classifiers Performance -- 3.5.1 Example-Based Metrics -- 3.5.2 Label-based Metrics -- References -- 4 Transformation-Based Classifiers -- 4.1 Introduction -- 4.2 Multilabel Data Transformation Approaches |
| Title | Multilabel Classification : Problem Analysis, Metrics and Techniques |
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