Multi-Label Feature Selection Using Adaptive and Transformed Relevance
Multi-label learning has emerged as a crucial paradigm in data analysis, addressing scenarios where instances are associated with multiple class labels simultaneously. With the growing prevalence of multi-label data across diverse applications, such as text and image classification, the significance of multi-label feature selection has become increasingly evident. This paper presents a novel information-theoretical filter-based multi-label feature selection, called ATR, with a new heuristic function. Incorporating a combinations of algorithm adaptation and problem transformation approaches, ATR ranks features considering individual labels as well as abstract label space discriminative powers. Our experimental studies encompass twelve benchmarks spanning various domains, demonstrating the superiority of our approach over ten state-of-the-art information-theoretical filter-based multi-label feature selection methods across six evaluation metrics. Furthermore, our experiments affirm the scalability of ATR for benchmarks characterized by extensive feature and label spaces. The codes are available at https://github.com/Sadegh28/ATR
Code (1)
Tasks
feature selectionimage-classificationImage ClassificationMulti-Label LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Adaptive Collaborative Sot Label Learning for Unsupervised Multi-view Feature Selection
Unsupervised multi-view feature selection aims to select informative features with multi-view features and unsupervised learning. It is a challenging problem due to the absence of explicit semantic supervision. Recently…
feature selectiongraph constructionPseudo LabelAdaptive Collaborative Correlation Learning-based Semi-Supervised Multi-Label Feature Selection
Semi-supervised multi-label feature selection has recently been developed to solve the curse of dimensionality problem in high-dimensional multi-label data with certain samples missing labels. Although many efforts have …
feature selectionMissing LabelsregressionYNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection
In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection. In detail, the word embedding is learned from Embeddings from Language Model (ELMO) to extract fea…
Language ModelingLanguage ModellingOrdinal ClassificationADSEL: Adaptive dual self-expression learning for EEG feature selection via incomplete multi-dimensional emotional tagging
EEG based multi-dimension emotion recognition has attracted substantial research interest in human computer interfaces. However, the high dimensionality of EEG features, coupled with limited sample sizes, frequently lead…
Emotion RecognitionGPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection
As artificial intelligence methods are increasingly applied to complex task scenarios, high dimensional multi-label learning has emerged as a prominent research focus. At present, the curse of dimensionality remains one …
feature selectionMulti-Label Learning