scholarly journals On the Stratification of Multi-label Data

2011 ◽  
pp. 145-158 ◽  
Author(s):  
Konstantinos Sechidis ◽  
Grigorios Tsoumakas ◽  
Ioannis Vlahavas
Keyword(s):  
Label Data

scholarly journals Multi-Label Data Fusion to Support Agricultural Vulnerability Assessments

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ivan Dario Lopez ◽  
Apolinar Figueroa ◽  
Juan Carlos Corrales
Keyword(s):  
Data Fusion ◽  
Label Data ◽  
Agricultural Vulnerability ◽  
Vulnerability Assessments

A novel feature selection approach with Pareto optimality for multi-label data

2021 ◽  
Author(s):  
Guohe Li ◽  
Yong Li ◽  
Yifeng Zheng ◽  
Ying Li ◽  
Yunfeng Hong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Pareto Optimality ◽  
Label Data ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals Learning from multi-label data with interactivity constraints: An extensive experimental study

2015 ◽  
Vol 42 (13) ◽  
pp. 5723-5736 ◽  
Author(s):  
Noureddine-Yassine Nair-Benrekia ◽  
Pascale Kuntz ◽  
Frank Meyer
Keyword(s):  
Experimental Study ◽  
Label Data

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Awder Mohammed Ahmed ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Feature Selection ◽  
Swarm Intelligence ◽  
Real World ◽  
Computational Cost ◽  
Computing Methods ◽  
Open Problems ◽  
Label Data ◽  
Filter Methods ◽  
Wide Range ◽  
Classification Tasks

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. Filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers to deal with this issue. Most of the existing researches and review papers dealing with feature selection in single-label data. While, recently multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining, and bioinformatics. Moreover, researchers have recently focused on applying swarm intelligence methods in selecting prominent features of multi-label data. To the best of our knowledge, there is no review paper that reviews swarm intelligence-based methods for multi-label feature selection. Thus, in this paper, we provide a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for multi-label classification tasks. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures, and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works, and at the end of this survey, some challenges, issues, and open problems of this field are introduced to be considered by researchers in the future.

A new species of Liophloeothrips (Thysanoptera, Phlaeothripidae) from leaf litter from Iran

Zootaxa ◽  
2019 ◽  
Vol 4571 (1) ◽  
pp. 138
Author(s):  
MAHSA HAKIMARA ◽  
KAMBIZ MINAEI ◽  
SABER SADEGHI ◽  
LAURENCE MOUND
Keyword(s):  
New Species ◽  
Leaf Litter ◽  
Type Species ◽  
Single Species ◽  
The Other ◽  
Single Female ◽  
Label Data ◽  
Fungal Hyphae ◽  
Plant Families ◽  
A New Species

Of the 16 species listed in the genus Liophloeothrips (ThripsWiki 2018), 13 are known only from India, and all of these are phytophagous with some inducing galls in various plant families (Tyagi & Kumar 2011). However, the biology of the type species, L. glaber, as well as that of the other two species, L. hungaricus and L. pulchrisetis, remains in doubt. Each of these three species is from Europe, with L. pulchrisetis known from a single female, L. glaber from two specimens, and hungaricus recorded from Hungary, Finland and Iran on a very few individuals (Minaei & Mound 2014). The record of L. hungaricus from Iran was published without any information concerning the locality, date of collection, or number of specimens (Mortazawiha 1995). However, Minaei and Mound (2014) pointed out that the slide label data of L. hungaricus specimens from Europe suggested that this species is associated with the bark of certain Salicaceae. Moreover, they indicated the possibility that the three names might actually represent a single species, although the male of L. glabrus has a sternal pore plate whereas this is apparently absent in hungaricus. Given the few known specimens, it is not possible to know if these thrips live under bark and feed on fungal hyphae, or if the few specimens collected were actually leaf-feeders that were sheltering under bark. In this paper, a new species of the genus is described from southern Iran, based on both sexes. These specimens were extracted from leaf litter using a Berlese funnel, thus again it is not possible to be certain if the species is part of the community of fungus-feeding litter thrips, or if the specimens were merely sheltering. 

Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data

2020 ◽  
pp. 1-1
Author(s):  
Guoli Song ◽  
Shuhui Wang ◽  
Qingming Huang ◽  
Qi Tian
Keyword(s):  
Partial Correlation ◽  
Feature Representation ◽  
Label Data

scholarly journals Learning from Weak-Label Data: A Deep Forest Expedition

2020 ◽  
Vol 34 (04) ◽  
pp. 6251-6258
Author(s):  
Qian-Wei Wang ◽  
Liang Yang ◽  
Yu-Feng Li
Keyword(s):  
Cross Validation ◽  
State Of The Art ◽  
Frequency Estimation ◽  
Ground Truth ◽  
Layer By Layer ◽  
Label Data ◽  
Information Layer ◽  
Deep Forest ◽  
Class Labels ◽  
Number Of Classes

Weak-label learning deals with the problem where each training example is associated with multiple ground-truth labels simultaneously but only partially provided. This circumstance is frequently encountered when the number of classes is very large or when there exists a large ambiguity between class labels, and significantly influences the performance of multi-label learning. In this paper, we propose LCForest, which is the first tree ensemble based deep learning method for weak-label learning. Rather than formulating the problem as a regularized framework, we employ the recently proposed cascade forest structure, which processes information layer-by-layer, and endow it with the ability of exploiting from weak-label data by a concise and highly efficient label complement structure. Specifically, in each layer, the label vector of each instance from testing-fold is modified with the predictions of random forests trained with the corresponding training-fold. Since the ground-truth label matrix is inaccessible, we can not estimate the performance via cross-validation directly. In order to control the growth of cascade forest, we adopt label frequency estimation and the complement flag mechanism. Experiments show that the proposed LCForest method compares favorably against the existing state-of-the-art multi-label and weak-label learning methods.

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