• # ML-KNN

• Referenced in 74 articles [sw12923]
• lazy learning approach to multi-label learning. Multi-label learning originated from the investigation ... several predefined topics simultaneously. In multi-label learning, the training set is composed of instances ... Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis ... superior performance to some well-established multi-label learning algorithms...
• # MULAN

• Referenced in 65 articles [sw08062]
• library for multi-label learning. MULAN is a Java library for learning from multi-label ... reduction algorithms, as well as algorithms for learning from hierarchically structured labels. In addition...
• # iLoc-Virus

• Referenced in 31 articles [sw22417]
• iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins ... this paper, by introducing the ”multi-label scale” and by hybridizing the gene ontology information...
• # iLoc-Animal

• Referenced in 20 articles [sw22427]
• iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins. Predicting ... problem, particularly when query proteins have multi-label features meaning that they may simultaneously exist ... depth studies. By introducing the ”multi-label learning” approach, a new predictor, called iLoc-Animal ... systems containing both single- and multi-label animal (metazoan except human) proteins. Meanwhile, to measure...
• # MLTSVM

• Referenced in 7 articles [sw27170]
• novel twin support vector machine to multi-label learning. Multi-label learning paradigm, which aims ... this paper, we propose a novel multi-label twin support vector machine (MLTSVM) for multi...
• # node2vec

• Referenced in 79 articles [sw27202]
• exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec ... state-of-the-art techniques on multi-label classification and link prediction in several real ... work represents a new way for efficiently learning state-of-the-art task-independent representations...
• # DeepWalk

• Referenced in 63 articles [sw39604]
• information obtained from truncated random walks to learn latent representations by treating walks ... DeepWalk’s latent representations on several multi-label network classification tasks for social networks such ... higher than competing methods when labeled data is sparse. In some experiments, DeepWalk’s representations ... also scalable. It is an online learning algorithm which builds useful incremental results...
• # utiml

• Referenced in 2 articles [sw27783]
• package utiml: Utilities for Multi-Label Learning. Multi-label learning strategies and others procedures ... package provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold...
• # DiSMEC

• Referenced in 4 articles [sw30154]
• multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even ... distribution, i.e. a large fraction of labels have very few positive instances in the data ... extreme multi-label classification attempt to capture correlation among labels by embedding the label matrix ... power-law distributed extremely large and diverse label spaces, structural assumptions such as low rank...
• # CNN-RNN

• Referenced in 8 articles [sw28401]
• image. Traditional approaches to multi-label image classification learn independent classifiers for each category ... working well, fail to explicitly exploit the label dependencies in an image. In this paper ... framework learns a joint image-label embedding to characterize the semantic label dependency as well ... performance than the state-of-the-art multi-label classification model...
• # ATPboost

• Referenced in 4 articles [sw28626]
• Unlike many approaches that use multi-label setting, the learning is implemented as binary classification ... XGBoost gradient boosting algorithm. Learning in the binary setting however requires negative examples, which ... show significant improvement over the multi-label approach...
• # MEKA

• Referenced in 12 articles [sw15429]
• WEKA. Multi-label classification has rapidly attracted interest in the machine learning literature, and there ... variety of methods for this type of learning. We present MEKA: an open-source Java ... practical application, and a wealth of multi-label classifiers, evaluation metrics, and tools for multi...
• # HIN2Vec

• Referenced in 6 articles [sw37750]
• examined. To validate our ideas, we learn latent vectors of nodes using four large-scale ... them as features for multi-label node classification and link prediction applications on those networks ... outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE ... micro$-$f_1\$ in multi-label node classification...
• # ALiPy

• Referenced in 4 articles [sw33954]
• learning framework, including data process, active selection, label query, results visualization, etc. In addition ... than 20 state-of-the-art active learning algorithms, ALiPy also supports users to easily ... under different active learning settings, such as AL for multi-label data, AL with noisy...
• # LNEMLC

• Referenced in 0 articles [sw28400]
• multi-label classification focus on effective adaptation or transformation of existing binary and multi-class ... unseen label combinations. To address these issues we propose a new multi-label classification scheme ... Label Network Embedding for Multi-Label Classification, that embeds the label network and uses ... input space in learning and inference of any base multi-label classifier. The approach allows...