Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data Article Swipe
Payel Sadhukhan
,
Arjun Pakrashi
,
Sarbani Palit
,
Brian Mac Namee
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5220/0011901200003393
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5220/0011901200003393
There is often a mixture of very frequent labels and very infrequent labels in multi-label datatsets. This variation in label frequency, a type class imbalance, creates a significant challenge for building efficient multi-label classification algorithms. In this paper, we tackle this problem by proposing a minority class oversampling scheme, UCLSO, which integrates Unsupervised Clustering and Label-Specific data Oversampling. Clustering is performed to find out the key distinct and locally connected regions of a multi-label dataset (irrespective of the label information). Next, for each label, we explore the distributions of minority points in the cluster sets. Only the minority points within a cluster are used to generate the synthetic minority points that are used for oversampling. Even though the cluster set is the same across all labels, the distributions of the synthetic minority points will vary across the labels. The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently. Experiments using 12 multi-label datasets and several multi-label algorithms show that the proposed method performed very well compared to the other competing algorithms.
Related Topics
Concepts
Oversampling
Cluster analysis
Computer science
Class (philosophy)
Artificial intelligence
Set (abstract data type)
Multi-label classification
Pattern recognition (psychology)
Machine learning
Data mining
Relevance (law)
Bandwidth (computing)
Political science
Programming language
Computer network
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0011901200003393
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204714478
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3204714478Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5220/0011901200003393Digital Object Identifier
- Title
-
Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Payel Sadhukhan, Arjun Pakrashi, Sarbani Palit, Brian Mac NameeList of authors in order
- Landing page
-
https://doi.org/10.5220/0011901200003393Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5220/0011901200003393Direct OA link when available
- Concepts
-
Oversampling, Cluster analysis, Computer science, Class (philosophy), Artificial intelligence, Set (abstract data type), Multi-label classification, Pattern recognition (psychology), Machine learning, Data mining, Relevance (law), Bandwidth (computing), Political science, Programming language, Computer network, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3204714478 |
|---|---|
| doi | https://doi.org/10.5220/0011901200003393 |
| ids.doi | https://doi.org/10.5220/0011901200003393 |
| ids.mag | 3204714478 |
| ids.openalex | https://openalex.org/W3204714478 |
| fwci | 0.0 |
| type | article |
| title | Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 498 |
| biblio.first_page | 489 |
| topics[0].id | https://openalex.org/T11550 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Text and Document Classification Technologies |
| topics[1].id | https://openalex.org/T11269 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9861999750137329 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Algorithms and Data Compression |
| topics[2].id | https://openalex.org/T11644 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9861000180244446 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1710 |
| topics[2].subfield.display_name | Information Systems |
| topics[2].display_name | Spam and Phishing Detection |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C197323446 |
| concepts[0].level | 3 |
| concepts[0].score | 0.9505425691604614 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q331222 |
| concepts[0].display_name | Oversampling |
| concepts[1].id | https://openalex.org/C73555534 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7762590646743774 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[1].display_name | Cluster analysis |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6992543935775757 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2777212361 |
| concepts[3].level | 2 |
| concepts[3].score | 0.589901864528656 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q5127848 |
| concepts[3].display_name | Class (philosophy) |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.579808235168457 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C177264268 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5592879056930542 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[5].display_name | Set (abstract data type) |
| concepts[6].id | https://openalex.org/C2776482837 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5504190325737 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q3553958 |
| concepts[6].display_name | Multi-label classification |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5483471155166626 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.47698289155960083 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.43582671880722046 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C158154518 |
| concepts[10].level | 2 |
| concepts[10].score | 0.41598737239837646 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7310970 |
| concepts[10].display_name | Relevance (law) |
| concepts[11].id | https://openalex.org/C2776257435 |
| concepts[11].level | 2 |
| concepts[11].score | 0.06199488043785095 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1576430 |
| concepts[11].display_name | Bandwidth (computing) |
| concepts[12].id | https://openalex.org/C17744445 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[12].display_name | Political science |
| concepts[13].id | https://openalex.org/C199360897 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[13].display_name | Programming language |
| concepts[14].id | https://openalex.org/C31258907 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[14].display_name | Computer network |
| concepts[15].id | https://openalex.org/C199539241 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[15].display_name | Law |
| keywords[0].id | https://openalex.org/keywords/oversampling |
| keywords[0].score | 0.9505425691604614 |
| keywords[0].display_name | Oversampling |
| keywords[1].id | https://openalex.org/keywords/cluster-analysis |
| keywords[1].score | 0.7762590646743774 |
| keywords[1].display_name | Cluster analysis |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6992543935775757 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/class |
| keywords[3].score | 0.589901864528656 |
| keywords[3].display_name | Class (philosophy) |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.579808235168457 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/set |
| keywords[5].score | 0.5592879056930542 |
| keywords[5].display_name | Set (abstract data type) |
| keywords[6].id | https://openalex.org/keywords/multi-label-classification |
| keywords[6].score | 0.5504190325737 |
| keywords[6].display_name | Multi-label classification |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.5483471155166626 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.47698289155960083 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.43582671880722046 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/relevance |
| keywords[10].score | 0.41598737239837646 |
| keywords[10].display_name | Relevance (law) |
| keywords[11].id | https://openalex.org/keywords/bandwidth |
| keywords[11].score | 0.06199488043785095 |
| keywords[11].display_name | Bandwidth (computing) |
| language | en |
| locations[0].id | doi:10.5220/0011901200003393 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the 15th International Conference on Agents and Artificial Intelligence |
| locations[0].landing_page_url | https://doi.org/10.5220/0011901200003393 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5015761218 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7795-3385 |
| authorships[0].author.display_name | Payel Sadhukhan |
| authorships[0].affiliations[0].raw_affiliation_string | Institute for Advancing Intelligence, TCG CREST, Kolkata, India, --- Select a Country --- |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Payel Sadhukhan |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Institute for Advancing Intelligence, TCG CREST, Kolkata, India, --- Select a Country --- |
| authorships[1].author.id | https://openalex.org/A5005100701 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9605-6839 |
| authorships[1].author.display_name | Arjun Pakrashi |
| authorships[1].countries | IE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I100930933 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computer Science, University College, Dublin, Ireland, --- Select a Country --- |
| authorships[1].institutions[0].id | https://openalex.org/I100930933 |
| authorships[1].institutions[0].ror | https://ror.org/05m7pjf47 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I100930933 |
| authorships[1].institutions[0].country_code | IE |
| authorships[1].institutions[0].display_name | University College Dublin |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Arjun Pakrashi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computer Science, University College, Dublin, Ireland, --- Select a Country --- |
| authorships[2].author.id | https://openalex.org/A5044454651 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-4105-6452 |
| authorships[2].author.display_name | Sarbani Palit |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I6498739 |
| authorships[2].affiliations[0].raw_affiliation_string | Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India, --- Select a Country --- |
| authorships[2].institutions[0].id | https://openalex.org/I6498739 |
| authorships[2].institutions[0].ror | https://ror.org/00q2w1j53 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I6498739 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Indian Statistical Institute |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Sarbani Palit |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India, --- Select a Country --- |
| authorships[3].author.id | https://openalex.org/A5084251444 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2518-0274 |
| authorships[3].author.display_name | Brian Mac Namee |
| authorships[3].countries | IE |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I100930933 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computer Science, University College, Dublin, Ireland, --- Select a Country --- |
| authorships[3].institutions[0].id | https://openalex.org/I100930933 |
| authorships[3].institutions[0].ror | https://ror.org/05m7pjf47 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I100930933 |
| authorships[3].institutions[0].country_code | IE |
| authorships[3].institutions[0].display_name | University College Dublin |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Brian Namee |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Computer Science, University College, Dublin, Ireland, --- Select a Country --- |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5220/0011901200003393 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11550 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Text and Document Classification Technologies |
| related_works | https://openalex.org/W2766503024, https://openalex.org/W4206637278, https://openalex.org/W2781247653, https://openalex.org/W4386005305, https://openalex.org/W3082051559, https://openalex.org/W1682621979, https://openalex.org/W4386214543, https://openalex.org/W1969988626, https://openalex.org/W3173198409, https://openalex.org/W2300921526 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5220/0011901200003393 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Proceedings of the 15th International Conference on Agents and Artificial Intelligence |
| best_oa_location.landing_page_url | https://doi.org/10.5220/0011901200003393 |
| primary_location.id | doi:10.5220/0011901200003393 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the 15th International Conference on Agents and Artificial Intelligence |
| primary_location.landing_page_url | https://doi.org/10.5220/0011901200003393 |
| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2090630554, https://openalex.org/W2148143831, https://openalex.org/W2052684427, https://openalex.org/W1045349118, https://openalex.org/W2156935079, https://openalex.org/W2110075577, https://openalex.org/W2119466907, https://openalex.org/W2405989770, https://openalex.org/W2966122517, https://openalex.org/W2117910203, https://openalex.org/W2149684865, https://openalex.org/W2155440340, https://openalex.org/W2166912588, https://openalex.org/W2012995268, https://openalex.org/W1999954155, https://openalex.org/W2614937618, https://openalex.org/W2074888575, https://openalex.org/W2767245172, https://openalex.org/W2138290126, https://openalex.org/W2604201385, https://openalex.org/W2992861029, https://openalex.org/W146125889, https://openalex.org/W2588628126, https://openalex.org/W2774679738, https://openalex.org/W66588809, https://openalex.org/W2135452169, https://openalex.org/W2165466912, https://openalex.org/W2285859684, https://openalex.org/W2115386194, https://openalex.org/W2141072099, https://openalex.org/W2594029683, https://openalex.org/W2143854982, https://openalex.org/W2136623223, https://openalex.org/W3028947187, https://openalex.org/W3015464002, https://openalex.org/W2114315281, https://openalex.org/W1679220824, https://openalex.org/W2118978333, https://openalex.org/W2253239179 |
| referenced_works_count | 39 |
| abstract_inverted_index.a | 3, 21, 26, 44, 72, 100 |
| abstract_inverted_index.12 | 165 |
| abstract_inverted_index.In | 35 |
| abstract_inverted_index.by | 42 |
| abstract_inverted_index.in | 13, 18, 91 |
| abstract_inverted_index.is | 1, 59, 120, 141 |
| abstract_inverted_index.of | 5, 71, 76, 88, 128, 146, 159 |
| abstract_inverted_index.to | 61, 104, 155, 181 |
| abstract_inverted_index.we | 38, 84 |
| abstract_inverted_index.The | 138 |
| abstract_inverted_index.all | 124 |
| abstract_inverted_index.and | 9, 54, 67, 151, 168 |
| abstract_inverted_index.are | 102, 111, 153 |
| abstract_inverted_index.for | 29, 81, 113 |
| abstract_inverted_index.key | 65 |
| abstract_inverted_index.out | 63 |
| abstract_inverted_index.set | 119, 145 |
| abstract_inverted_index.the | 64, 77, 86, 92, 96, 106, 117, 121, 126, 129, 136, 144, 157, 174, 182 |
| abstract_inverted_index.Even | 115 |
| abstract_inverted_index.Only | 95 |
| abstract_inverted_index.This | 16 |
| abstract_inverted_index.data | 56 |
| abstract_inverted_index.each | 82, 160 |
| abstract_inverted_index.find | 62 |
| abstract_inverted_index.same | 122 |
| abstract_inverted_index.show | 172 |
| abstract_inverted_index.that | 110, 173 |
| abstract_inverted_index.this | 36, 40 |
| abstract_inverted_index.type | 22 |
| abstract_inverted_index.used | 103, 112 |
| abstract_inverted_index.vary | 134 |
| abstract_inverted_index.very | 6, 10, 178 |
| abstract_inverted_index.well | 179 |
| abstract_inverted_index.will | 133 |
| abstract_inverted_index.with | 143 |
| abstract_inverted_index.Next, | 80 |
| abstract_inverted_index.There | 0 |
| abstract_inverted_index.class | 23, 46 |
| abstract_inverted_index.label | 19, 78, 161 |
| abstract_inverted_index.often | 2 |
| abstract_inverted_index.other | 183 |
| abstract_inverted_index.sets. | 94 |
| abstract_inverted_index.using | 164 |
| abstract_inverted_index.which | 50 |
| abstract_inverted_index.UCLSO, | 49 |
| abstract_inverted_index.across | 123, 135 |
| abstract_inverted_index.label, | 83 |
| abstract_inverted_index.labels | 8, 12 |
| abstract_inverted_index.method | 176 |
| abstract_inverted_index.paper, | 37 |
| abstract_inverted_index.points | 90, 98, 109, 132 |
| abstract_inverted_index.tackle | 39 |
| abstract_inverted_index.though | 116 |
| abstract_inverted_index.within | 99 |
| abstract_inverted_index.cluster | 93, 101, 118 |
| abstract_inverted_index.creates | 25 |
| abstract_inverted_index.dataset | 74, 140 |
| abstract_inverted_index.explore | 85 |
| abstract_inverted_index.labels, | 125 |
| abstract_inverted_index.labels. | 137 |
| abstract_inverted_index.locally | 68 |
| abstract_inverted_index.mixture | 4 |
| abstract_inverted_index.points, | 150 |
| abstract_inverted_index.predict | 156 |
| abstract_inverted_index.problem | 41 |
| abstract_inverted_index.regions | 70 |
| abstract_inverted_index.scheme, | 48 |
| abstract_inverted_index.several | 169 |
| abstract_inverted_index.trained | 154 |
| abstract_inverted_index.building | 30 |
| abstract_inverted_index.compared | 180 |
| abstract_inverted_index.datasets | 167 |
| abstract_inverted_index.distinct | 66 |
| abstract_inverted_index.frequent | 7 |
| abstract_inverted_index.generate | 105 |
| abstract_inverted_index.minority | 45, 89, 97, 108, 131, 149 |
| abstract_inverted_index.proposed | 175 |
| abstract_inverted_index.training | 139 |
| abstract_inverted_index.augmented | 142 |
| abstract_inverted_index.challenge | 28 |
| abstract_inverted_index.competing | 184 |
| abstract_inverted_index.connected | 69 |
| abstract_inverted_index.efficient | 31 |
| abstract_inverted_index.performed | 60, 177 |
| abstract_inverted_index.proposing | 43 |
| abstract_inverted_index.relevance | 158 |
| abstract_inverted_index.synthetic | 107, 130, 148 |
| abstract_inverted_index.variation | 17 |
| abstract_inverted_index.Clustering | 53, 58 |
| abstract_inverted_index.algorithms | 171 |
| abstract_inverted_index.datatsets. | 15 |
| abstract_inverted_index.frequency, | 20 |
| abstract_inverted_index.imbalance, | 24 |
| abstract_inverted_index.infrequent | 11 |
| abstract_inverted_index.integrates | 51 |
| abstract_inverted_index.Experiments | 163 |
| abstract_inverted_index.algorithms. | 34, 185 |
| abstract_inverted_index.classifiers | 152 |
| abstract_inverted_index.multi-label | 14, 32, 73, 166, 170 |
| abstract_inverted_index.significant | 27 |
| abstract_inverted_index.Unsupervised | 52 |
| abstract_inverted_index.oversampling | 47 |
| abstract_inverted_index.(irrespective | 75 |
| abstract_inverted_index.Oversampling. | 57 |
| abstract_inverted_index.distributions | 87, 127 |
| abstract_inverted_index.information). | 79 |
| abstract_inverted_index.oversampling. | 114 |
| abstract_inverted_index.Label-Specific | 55 |
| abstract_inverted_index.classification | 33 |
| abstract_inverted_index.independently. | 162 |
| abstract_inverted_index.label-specific | 147 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.00250264 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |