Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Paired Comparisons Method for Solving Multi-Label Learning Problem
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Expert Systems with Applications: An International Journal
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Label ranking by learning pairwise preferences
Artificial Intelligence
Decision trees for hierarchical multi-label classification
Machine Learning
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
An Empirical Study of Lazy Multilabel Classification Algorithms
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Classification of Multi-labeled Data: A Generative Approach
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Representing uncertainty on set-valued variables using belief functions
Artificial Intelligence
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors
Expert Systems with Applications: An International Journal
Classifier chains for multi-label classification
Machine Learning
Semantic Annotation and Retrieval of Music and Sound Effects
IEEE Transactions on Audio, Speech, and Language Processing
Neighborhood rough sets based multi-label classification for automatic image annotation
International Journal of Approximate Reasoning
Random block coordinate descent method for multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called multi-label classification using rough sets (MLRS) and MLRS using local correlation (MLRS-LC). The proposed algorithms consider two important factors that affect the accuracy of prediction, namely the correlation among the labels and the uncertainty that exists within the mapping between the feature space and the label space. MLRS provides a global view at the label correlation while MLRS-LC deals with the label correlation at the local level. Given a new instance, MLRS determines its location and then computes the probabilities of labels according to its location. The MLRS-LC first finds out its topic and then the probabilities of new instance belonging to each class is calculated in related topic. A series of experiments reported for seven multi-label datasets show that MLRS and MLRS-LC achieve promising performance when compared with some well-known multi-label learning algorithms.