An Earth mover's distance-based undersampling approach for handling class-imbalanced data Article Swipe
Gillala Rekha
,
Amit Kumar Tyagi
,
V. Krishna Reddy
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1504/ijiids.2020.10031612
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1504/ijiids.2020.10031612
Imbalanced datasets typically make prediction accuracy difficult. Most of the real-world data are imbalanced in nature. The traditional classifiers assume a well-balanced class distribution for training data but in practical datasets show up an imbalance, thus obscure a classifier and degrade its capability to learn from such imbalanced datasets. Data pre-processing approaches address this concern by using either random undersampling or oversampling techniques. In this paper, we introduce Earth mover's distance (EMD), as a similarity measure, to find the samples similar in nature and eliminate them as redundant from the dataset. Earth mover's distance has received a lot of attention in wide areas such as computer vision, image retrieval, machine learning, etc. The Earth mover's distance-based undersampling approach provides a solution at the data level to eliminate the redundant instances in majority samples without any loss of valuable information. This method is implemented with five conventional classifiers and one ensemble technique respectively, like C4.5 decision tree (DT), k-nearest neighbour (k-NN), multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB) and AdaBoost technique. The proposed method yields a superior performance on 21 datasets from Keel repository.
Related Topics
Concepts
Undersampling
Earth mover's distance
Computer science
Artificial intelligence
Support vector machine
AdaBoost
Random forest
Oversampling
Machine learning
Naive Bayes classifier
Pattern recognition (psychology)
Decision tree
Data mining
Classifier (UML)
Hellinger distance
Mathematics
Statistics
Bandwidth (computing)
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1504/ijiids.2020.10031612
- OA Status
- diamond
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3081285507
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3081285507Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1504/ijiids.2020.10031612Digital Object Identifier
- Title
-
An Earth mover's distance-based undersampling approach for handling class-imbalanced dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Gillala Rekha, Amit Kumar Tyagi, V. Krishna ReddyList of authors in order
- Landing page
-
https://doi.org/10.1504/ijiids.2020.10031612Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1504/ijiids.2020.10031612Direct OA link when available
- Concepts
-
Undersampling, Earth mover's distance, Computer science, Artificial intelligence, Support vector machine, AdaBoost, Random forest, Oversampling, Machine learning, Naive Bayes classifier, Pattern recognition (psychology), Decision tree, Data mining, Classifier (UML), Hellinger distance, Mathematics, Statistics, Bandwidth (computing), Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3081285507 |
|---|---|
| doi | https://doi.org/10.1504/ijiids.2020.10031612 |
| ids.doi | https://doi.org/10.1504/ijiids.2020.10031612 |
| ids.mag | 3081285507 |
| ids.openalex | https://openalex.org/W3081285507 |
| fwci | 0.2937191 |
| type | article |
| title | An Earth mover's distance-based undersampling approach for handling class-imbalanced data |
| biblio.issue | 2/3/4 |
| biblio.volume | 13 |
| biblio.last_page | 376 |
| biblio.first_page | 376 |
| topics[0].id | https://openalex.org/T11652 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9991999864578247 |
| 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 | Imbalanced Data Classification Techniques |
| topics[1].id | https://openalex.org/T11396 |
| topics[1].field.id | https://openalex.org/fields/36 |
| topics[1].field.display_name | Health Professions |
| topics[1].score | 0.9848999977111816 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3605 |
| topics[1].subfield.display_name | Health Information Management |
| topics[1].display_name | Artificial Intelligence in Healthcare |
| topics[2].id | https://openalex.org/T14319 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9466999769210815 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Currency Recognition and Detection |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C136536468 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9673742055892944 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1225894 |
| concepts[0].display_name | Undersampling |
| concepts[1].id | https://openalex.org/C82668687 |
| concepts[1].level | 2 |
| concepts[1].score | 0.8391362428665161 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3046456 |
| concepts[1].display_name | Earth mover's distance |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.8216756582260132 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.703406035900116 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C12267149 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6482136845588684 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[4].display_name | Support vector machine |
| concepts[5].id | https://openalex.org/C141404830 |
| concepts[5].level | 3 |
| concepts[5].score | 0.6088552474975586 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2823869 |
| concepts[5].display_name | AdaBoost |
| concepts[6].id | https://openalex.org/C169258074 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5993722081184387 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[6].display_name | Random forest |
| concepts[7].id | https://openalex.org/C197323446 |
| concepts[7].level | 3 |
| concepts[7].score | 0.5706154704093933 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q331222 |
| concepts[7].display_name | Oversampling |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.5634248852729797 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C52001869 |
| concepts[9].level | 3 |
| concepts[9].score | 0.558353066444397 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q812530 |
| concepts[9].display_name | Naive Bayes classifier |
| concepts[10].id | https://openalex.org/C153180895 |
| concepts[10].level | 2 |
| concepts[10].score | 0.5284744501113892 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[10].display_name | Pattern recognition (psychology) |
| concepts[11].id | https://openalex.org/C84525736 |
| concepts[11].level | 2 |
| concepts[11].score | 0.47899433970451355 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q831366 |
| concepts[11].display_name | Decision tree |
| concepts[12].id | https://openalex.org/C124101348 |
| concepts[12].level | 1 |
| concepts[12].score | 0.47772523760795593 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[12].display_name | Data mining |
| concepts[13].id | https://openalex.org/C95623464 |
| concepts[13].level | 2 |
| concepts[13].score | 0.4560856521129608 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[13].display_name | Classifier (UML) |
| concepts[14].id | https://openalex.org/C153024298 |
| concepts[14].level | 2 |
| concepts[14].score | 0.4246867001056671 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3030678 |
| concepts[14].display_name | Hellinger distance |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.09385481476783752 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C105795698 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[16].display_name | Statistics |
| concepts[17].id | https://openalex.org/C2776257435 |
| concepts[17].level | 2 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1576430 |
| concepts[17].display_name | Bandwidth (computing) |
| concepts[18].id | https://openalex.org/C31258907 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[18].display_name | Computer network |
| keywords[0].id | https://openalex.org/keywords/undersampling |
| keywords[0].score | 0.9673742055892944 |
| keywords[0].display_name | Undersampling |
| keywords[1].id | https://openalex.org/keywords/earth-movers-distance |
| keywords[1].score | 0.8391362428665161 |
| keywords[1].display_name | Earth mover's distance |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.8216756582260132 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.703406035900116 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/support-vector-machine |
| keywords[4].score | 0.6482136845588684 |
| keywords[4].display_name | Support vector machine |
| keywords[5].id | https://openalex.org/keywords/adaboost |
| keywords[5].score | 0.6088552474975586 |
| keywords[5].display_name | AdaBoost |
| keywords[6].id | https://openalex.org/keywords/random-forest |
| keywords[6].score | 0.5993722081184387 |
| keywords[6].display_name | Random forest |
| keywords[7].id | https://openalex.org/keywords/oversampling |
| keywords[7].score | 0.5706154704093933 |
| keywords[7].display_name | Oversampling |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.5634248852729797 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/naive-bayes-classifier |
| keywords[9].score | 0.558353066444397 |
| keywords[9].display_name | Naive Bayes classifier |
| keywords[10].id | https://openalex.org/keywords/pattern-recognition |
| keywords[10].score | 0.5284744501113892 |
| keywords[10].display_name | Pattern recognition (psychology) |
| keywords[11].id | https://openalex.org/keywords/decision-tree |
| keywords[11].score | 0.47899433970451355 |
| keywords[11].display_name | Decision tree |
| keywords[12].id | https://openalex.org/keywords/data-mining |
| keywords[12].score | 0.47772523760795593 |
| keywords[12].display_name | Data mining |
| keywords[13].id | https://openalex.org/keywords/classifier |
| keywords[13].score | 0.4560856521129608 |
| keywords[13].display_name | Classifier (UML) |
| keywords[14].id | https://openalex.org/keywords/hellinger-distance |
| keywords[14].score | 0.4246867001056671 |
| keywords[14].display_name | Hellinger distance |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.09385481476783752 |
| keywords[15].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1504/ijiids.2020.10031612 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S27556001 |
| locations[0].source.issn | 1751-5858, 1751-5866 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1751-5858 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal of Intelligent Information and Database Systems |
| locations[0].source.host_organization | https://openalex.org/P4310317825 |
| locations[0].source.host_organization_name | Inderscience Publishers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310317825 |
| locations[0].source.host_organization_lineage_names | Inderscience Publishers |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal of Intelligent Information and Database Systems |
| locations[0].landing_page_url | https://doi.org/10.1504/ijiids.2020.10031612 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5101890441 |
| authorships[0].author.orcid | https://orcid.org/0009-0003-4933-4255 |
| authorships[0].author.display_name | Gillala Rekha |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I875944469 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India |
| authorships[0].institutions[0].id | https://openalex.org/I875944469 |
| authorships[0].institutions[0].ror | https://ror.org/02k949197 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I875944469 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Koneru Lakshmaiah Education Foundation |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Gillala Rekha |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India |
| authorships[1].author.id | https://openalex.org/A5039774878 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2657-8700 |
| authorships[1].author.display_name | Amit Kumar Tyagi |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I876193797 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computing Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, India |
| authorships[1].institutions[0].id | https://openalex.org/I876193797 |
| authorships[1].institutions[0].ror | https://ror.org/00qzypv28 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I876193797 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Vellore Institute of Technology University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Amit Kumar Tyagi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computing Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, Tamil Nadu, India |
| authorships[2].author.id | https://openalex.org/A5011563218 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | V. Krishna Reddy |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I875944469 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India |
| authorships[2].institutions[0].id | https://openalex.org/I875944469 |
| authorships[2].institutions[0].ror | https://ror.org/02k949197 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I875944469 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Koneru Lakshmaiah Education Foundation |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | V. Krishna Reddy |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1504/ijiids.2020.10031612 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Earth mover's distance-based undersampling approach for handling class-imbalanced data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11652 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9991999864578247 |
| 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 | Imbalanced Data Classification Techniques |
| related_works | https://openalex.org/W4308469503, https://openalex.org/W32988189, https://openalex.org/W2904737874, https://openalex.org/W4389233021, https://openalex.org/W2399571531, https://openalex.org/W80466363, https://openalex.org/W2947132063, https://openalex.org/W4288337828, https://openalex.org/W4287816717, https://openalex.org/W4390415670 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1504/ijiids.2020.10031612 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S27556001 |
| best_oa_location.source.issn | 1751-5858, 1751-5866 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1751-5858 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal of Intelligent Information and Database Systems |
| best_oa_location.source.host_organization | https://openalex.org/P4310317825 |
| best_oa_location.source.host_organization_name | Inderscience Publishers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310317825 |
| best_oa_location.source.host_organization_lineage_names | Inderscience Publishers |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal of Intelligent Information and Database Systems |
| best_oa_location.landing_page_url | https://doi.org/10.1504/ijiids.2020.10031612 |
| primary_location.id | doi:10.1504/ijiids.2020.10031612 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S27556001 |
| primary_location.source.issn | 1751-5858, 1751-5866 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1751-5858 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal of Intelligent Information and Database Systems |
| primary_location.source.host_organization | https://openalex.org/P4310317825 |
| primary_location.source.host_organization_name | Inderscience Publishers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310317825 |
| primary_location.source.host_organization_lineage_names | Inderscience Publishers |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Intelligent Information and Database Systems |
| primary_location.landing_page_url | https://doi.org/10.1504/ijiids.2020.10031612 |
| publication_date | 2020-01-01 |
| publication_year | 2020 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 20, 37, 73, 96, 119, 177 |
| abstract_inverted_index.21 | 181 |
| abstract_inverted_index.In | 63 |
| abstract_inverted_index.an | 33 |
| abstract_inverted_index.as | 72, 86, 104 |
| abstract_inverted_index.at | 121 |
| abstract_inverted_index.by | 55 |
| abstract_inverted_index.in | 14, 28, 81, 100, 130 |
| abstract_inverted_index.is | 141 |
| abstract_inverted_index.of | 8, 98, 136 |
| abstract_inverted_index.on | 180 |
| abstract_inverted_index.or | 60 |
| abstract_inverted_index.to | 43, 76, 125 |
| abstract_inverted_index.up | 32 |
| abstract_inverted_index.we | 66 |
| abstract_inverted_index.The | 16, 112, 173 |
| abstract_inverted_index.and | 39, 83, 147, 170 |
| abstract_inverted_index.any | 134 |
| abstract_inverted_index.are | 12 |
| abstract_inverted_index.but | 27 |
| abstract_inverted_index.for | 24 |
| abstract_inverted_index.has | 94 |
| abstract_inverted_index.its | 41 |
| abstract_inverted_index.lot | 97 |
| abstract_inverted_index.one | 148 |
| abstract_inverted_index.the | 9, 78, 89, 122, 127 |
| abstract_inverted_index.(NB) | 169 |
| abstract_inverted_index.C4.5 | 153 |
| abstract_inverted_index.Data | 49 |
| abstract_inverted_index.Keel | 184 |
| abstract_inverted_index.Most | 7 |
| abstract_inverted_index.This | 139 |
| abstract_inverted_index.data | 11, 26, 123 |
| abstract_inverted_index.etc. | 111 |
| abstract_inverted_index.find | 77 |
| abstract_inverted_index.five | 144 |
| abstract_inverted_index.from | 45, 88, 183 |
| abstract_inverted_index.like | 152 |
| abstract_inverted_index.loss | 135 |
| abstract_inverted_index.make | 3 |
| abstract_inverted_index.show | 31 |
| abstract_inverted_index.such | 46, 103 |
| abstract_inverted_index.them | 85 |
| abstract_inverted_index.this | 53, 64 |
| abstract_inverted_index.thus | 35 |
| abstract_inverted_index.tree | 155 |
| abstract_inverted_index.wide | 101 |
| abstract_inverted_index.with | 143 |
| abstract_inverted_index.(DT), | 156 |
| abstract_inverted_index.Bayes | 168 |
| abstract_inverted_index.Earth | 68, 91, 113 |
| abstract_inverted_index.areas | 102 |
| abstract_inverted_index.class | 22 |
| abstract_inverted_index.image | 107 |
| abstract_inverted_index.learn | 44 |
| abstract_inverted_index.level | 124 |
| abstract_inverted_index.naive | 167 |
| abstract_inverted_index.using | 56 |
| abstract_inverted_index.(EMD), | 71 |
| abstract_inverted_index.(MLP), | 162 |
| abstract_inverted_index.(SVM), | 166 |
| abstract_inverted_index.assume | 19 |
| abstract_inverted_index.either | 57 |
| abstract_inverted_index.method | 140, 175 |
| abstract_inverted_index.nature | 82 |
| abstract_inverted_index.paper, | 65 |
| abstract_inverted_index.random | 58 |
| abstract_inverted_index.vector | 164 |
| abstract_inverted_index.yields | 176 |
| abstract_inverted_index.(k-NN), | 159 |
| abstract_inverted_index.address | 52 |
| abstract_inverted_index.concern | 54 |
| abstract_inverted_index.degrade | 40 |
| abstract_inverted_index.machine | 109, 165 |
| abstract_inverted_index.mover's | 69, 92, 114 |
| abstract_inverted_index.nature. | 15 |
| abstract_inverted_index.obscure | 36 |
| abstract_inverted_index.samples | 79, 132 |
| abstract_inverted_index.similar | 80 |
| abstract_inverted_index.support | 163 |
| abstract_inverted_index.vision, | 106 |
| abstract_inverted_index.without | 133 |
| abstract_inverted_index.AdaBoost | 171 |
| abstract_inverted_index.accuracy | 5 |
| abstract_inverted_index.approach | 117 |
| abstract_inverted_index.computer | 105 |
| abstract_inverted_index.dataset. | 90 |
| abstract_inverted_index.datasets | 1, 30, 182 |
| abstract_inverted_index.decision | 154 |
| abstract_inverted_index.distance | 70, 93 |
| abstract_inverted_index.ensemble | 149 |
| abstract_inverted_index.majority | 131 |
| abstract_inverted_index.measure, | 75 |
| abstract_inverted_index.proposed | 174 |
| abstract_inverted_index.provides | 118 |
| abstract_inverted_index.received | 95 |
| abstract_inverted_index.solution | 120 |
| abstract_inverted_index.superior | 178 |
| abstract_inverted_index.training | 25 |
| abstract_inverted_index.valuable | 137 |
| abstract_inverted_index.attention | 99 |
| abstract_inverted_index.datasets. | 48 |
| abstract_inverted_index.eliminate | 84, 126 |
| abstract_inverted_index.instances | 129 |
| abstract_inverted_index.introduce | 67 |
| abstract_inverted_index.k-nearest | 157 |
| abstract_inverted_index.learning, | 110 |
| abstract_inverted_index.neighbour | 158 |
| abstract_inverted_index.practical | 29 |
| abstract_inverted_index.redundant | 87, 128 |
| abstract_inverted_index.technique | 150 |
| abstract_inverted_index.typically | 2 |
| abstract_inverted_index.Imbalanced | 0 |
| abstract_inverted_index.approaches | 51 |
| abstract_inverted_index.capability | 42 |
| abstract_inverted_index.classifier | 38 |
| abstract_inverted_index.difficult. | 6 |
| abstract_inverted_index.imbalance, | 34 |
| abstract_inverted_index.imbalanced | 13, 47 |
| abstract_inverted_index.multilayer | 160 |
| abstract_inverted_index.perceptron | 161 |
| abstract_inverted_index.prediction | 4 |
| abstract_inverted_index.real-world | 10 |
| abstract_inverted_index.retrieval, | 108 |
| abstract_inverted_index.similarity | 74 |
| abstract_inverted_index.technique. | 172 |
| abstract_inverted_index.classifiers | 18, 146 |
| abstract_inverted_index.implemented | 142 |
| abstract_inverted_index.performance | 179 |
| abstract_inverted_index.repository. | 185 |
| abstract_inverted_index.techniques. | 62 |
| abstract_inverted_index.traditional | 17 |
| abstract_inverted_index.conventional | 145 |
| abstract_inverted_index.distribution | 23 |
| abstract_inverted_index.information. | 138 |
| abstract_inverted_index.oversampling | 61 |
| abstract_inverted_index.respectively, | 151 |
| abstract_inverted_index.undersampling | 59, 116 |
| abstract_inverted_index.well-balanced | 21 |
| abstract_inverted_index.distance-based | 115 |
| abstract_inverted_index.pre-processing | 50 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.63985635 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |