A Hybrid Ensemble Method with Focal Loss for Improved Forecasting Accuracy on Imbalanced Datasets Article Swipe

PDF

Xiaojun Guo , Wenxiu Cai , Cheng Yu , Jiaqi Chen , Liyang Wang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.20944/preprints202504.0831.v1

The inherent complexity and dynamic characteristics of diverse datasets present significant challenges for achieving high predictive accuracy in forecasting tasks. This study tackles these challenges by implementing a hybrid ensemble model aimed at enhancing predictive performance across imbalanced datasets. Using data from a competitive data source, the approach integrates LightGBM, XGBoost, and Logistic Regression models within a weighted ensemble framework to improve overall prediction accuracy. Data preprocessing techniques, including KNN imputation, Z-score normalization, and SMOTE, are employed to handle missing values, outliers, and class imbalances, ensuring a robust input for model training. The ensemble framework incorporates a Focal Loss function to specifically address class imbalances and refine prediction precision. Comparative analyses reveal that the proposed ensemble model consistently outperforms individual models in terms of accuracy, precision, recall, and AUC. This study offers a versatile and reliable solution for forecasting challenges, demonstrating enhanced robustness and broad applicability across domains.

Related Topics

Computer Science

Artificial Intelligence

Machine Learning

Statistics

Mathematics

Concepts

Computer science Artificial intelligence Machine learning Econometrics Statistics Mathematics

Metadata

Type: preprint
Language: en
Landing Page: https://doi.org/10.20944/preprints202504.0831.v1
PDF: https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4409400561

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4409400561

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.20944/preprints202504.0831.v1

Digital Object Identifier
Title: A Hybrid Ensemble Method with Focal Loss for Improved Forecasting Accuracy on Imbalanced Datasets

Work title
Type: preprint

OpenAlex work type
Language: en

Primary language
Publication year: 2025

Year of publication
Publication date: 2025-04-10

Full publication date if available
Authors: Xiaojun Guo, Wenxiu Cai, Cheng Yu, Jiaqi Chen, Liyang Wang

List of authors in order
Landing page: https://doi.org/10.20944/preprints202504.0831.v1

Publisher landing page
PDF URL: https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: green

Open access status per OpenAlex
OA URL: https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub

Direct OA link when available
Concepts: Computer science, Artificial intelligence, Machine learning, Econometrics, Statistics, Mathematics

Top concepts (fields/topics) attached by OpenAlex
Cited by: 0

Total citation count in OpenAlex
Related works (count): 10

Other works algorithmically related by OpenAlex

Full payload

id	https://openalex.org/W4409400561
doi	https://doi.org/10.20944/preprints202504.0831.v1
ids.doi	https://doi.org/10.20944/preprints202504.0831.v1
ids.openalex	https://openalex.org/W4409400561
fwci	0.0
type	preprint
title	A Hybrid Ensemble Method with Focal Loss for Improved Forecasting Accuracy on Imbalanced Datasets
biblio.issue
biblio.volume
biblio.last_page
biblio.first_page
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	0.9621999859809875
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
is_xpac	False
apc_list
apc_paid
concepts[0].id	https://openalex.org/C41008148
concepts[0].level	0
concepts[0].score	0.5099700689315796
concepts[0].wikidata	https://www.wikidata.org/wiki/Q21198
concepts[0].display_name	Computer science
concepts[1].id	https://openalex.org/C154945302
concepts[1].level	1
concepts[1].score	0.4527644217014313
concepts[1].wikidata	https://www.wikidata.org/wiki/Q11660
concepts[1].display_name	Artificial intelligence
concepts[2].id	https://openalex.org/C119857082
concepts[2].level	1
concepts[2].score	0.3405503034591675
concepts[2].wikidata	https://www.wikidata.org/wiki/Q2539
concepts[2].display_name	Machine learning
concepts[3].id	https://openalex.org/C149782125
concepts[3].level	1
concepts[3].score	0.3352709412574768
concepts[3].wikidata	https://www.wikidata.org/wiki/Q160039
concepts[3].display_name	Econometrics
concepts[4].id	https://openalex.org/C105795698
concepts[4].level	1
concepts[4].score	0.3203771710395813
concepts[4].wikidata	https://www.wikidata.org/wiki/Q12483
concepts[4].display_name	Statistics
concepts[5].id	https://openalex.org/C33923547
concepts[5].level	0
concepts[5].score	0.18411952257156372
concepts[5].wikidata	https://www.wikidata.org/wiki/Q395
concepts[5].display_name	Mathematics
keywords[0].id	https://openalex.org/keywords/computer-science
keywords[0].score	0.5099700689315796
keywords[0].display_name	Computer science
keywords[1].id	https://openalex.org/keywords/artificial-intelligence
keywords[1].score	0.4527644217014313
keywords[1].display_name	Artificial intelligence
keywords[2].id	https://openalex.org/keywords/machine-learning
keywords[2].score	0.3405503034591675
keywords[2].display_name	Machine learning
keywords[3].id	https://openalex.org/keywords/econometrics
keywords[3].score	0.3352709412574768
keywords[3].display_name	Econometrics
keywords[4].id	https://openalex.org/keywords/statistics
keywords[4].score	0.3203771710395813
keywords[4].display_name	Statistics
keywords[5].id	https://openalex.org/keywords/mathematics
keywords[5].score	0.18411952257156372
keywords[5].display_name	Mathematics
language	en
locations[0].id	doi:10.20944/preprints202504.0831.v1
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S6309402219
locations[0].source.issn
locations[0].source.type	repository
locations[0].source.is_oa	True
locations[0].source.issn_l
locations[0].source.is_core	False
locations[0].source.is_in_doaj	False
locations[0].source.display_name	Preprints.org
locations[0].source.host_organization
locations[0].source.host_organization_name
locations[0].source.host_organization_lineage	https://openalex.org/P4310310987
locations[0].source.host_organization_lineage_names	Multidisciplinary Digital Publishing Institute
locations[0].license	cc-by
locations[0].pdf_url	https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub
locations[0].version	acceptedVersion
locations[0].raw_type	posted-content
locations[0].license_id	https://openalex.org/licenses/cc-by
locations[0].is_accepted	True
locations[0].is_published	False
locations[0].raw_source_name
locations[0].landing_page_url	https://doi.org/10.20944/preprints202504.0831.v1
indexed_in	crossref
authorships[0].author.id	https://openalex.org/A5039235939
authorships[0].author.orcid	https://orcid.org/0000-0001-6694-3827
authorships[0].author.display_name	Xiaojun Guo
authorships[0].author_position	first
authorships[0].raw_author_name	Xiaojun Guo
authorships[0].is_corresponding	False
authorships[1].author.id	https://openalex.org/A5111376387
authorships[1].author.orcid
authorships[1].author.display_name	Wenxiu Cai
authorships[1].author_position	middle
authorships[1].raw_author_name	Wenxiu Cai
authorships[1].is_corresponding	False
authorships[2].author.id	https://openalex.org/A5026930299
authorships[2].author.orcid	https://orcid.org/0000-0003-1039-1300
authorships[2].author.display_name	Cheng Yu
authorships[2].author_position	middle
authorships[2].raw_author_name	Yu Cheng
authorships[2].is_corresponding	False
authorships[3].author.id	https://openalex.org/A5100434197
authorships[3].author.orcid	https://orcid.org/0000-0002-4069-193X
authorships[3].author.display_name	Jiaqi Chen
authorships[3].author_position	middle
authorships[3].raw_author_name	Jiaqi Chen
authorships[3].is_corresponding	False
authorships[4].author.id	https://openalex.org/A5089456973
authorships[4].author.orcid	https://orcid.org/0000-0002-9820-3310
authorships[4].author.display_name	Liyang Wang
authorships[4].author_position	last
authorships[4].raw_author_name	Liyang Wang
authorships[4].is_corresponding	False
has_content.pdf	True
has_content.grobid_xml	True
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub
open_access.oa_status	green
open_access.any_repository_has_fulltext	False
created_date	2025-10-10T00:00:00
display_name	A Hybrid Ensemble Method with Focal Loss for Improved Forecasting Accuracy on Imbalanced Datasets
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	0.9621999859809875
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/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W4387369504, https://openalex.org/W4394896187, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W3107602296, https://openalex.org/W4364306694, https://openalex.org/W4312192474, https://openalex.org/W4283697347
cited_by_count	0
locations_count	1
best_oa_location.id	doi:10.20944/preprints202504.0831.v1
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S6309402219
best_oa_location.source.issn
best_oa_location.source.type	repository
best_oa_location.source.is_oa	True
best_oa_location.source.issn_l
best_oa_location.source.is_core	False
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	Preprints.org
best_oa_location.source.host_organization
best_oa_location.source.host_organization_name
best_oa_location.source.host_organization_lineage	https://openalex.org/P4310310987
best_oa_location.source.host_organization_lineage_names	Multidisciplinary Digital Publishing Institute
best_oa_location.license	cc-by
best_oa_location.pdf_url	https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub
best_oa_location.version	acceptedVersion
best_oa_location.raw_type	posted-content
best_oa_location.license_id	https://openalex.org/licenses/cc-by
best_oa_location.is_accepted	True
best_oa_location.is_published	False
best_oa_location.raw_source_name
best_oa_location.landing_page_url	https://doi.org/10.20944/preprints202504.0831.v1
primary_location.id	doi:10.20944/preprints202504.0831.v1
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S6309402219
primary_location.source.issn
primary_location.source.type	repository
primary_location.source.is_oa	True
primary_location.source.issn_l
primary_location.source.is_core	False
primary_location.source.is_in_doaj	False
primary_location.source.display_name	Preprints.org
primary_location.source.host_organization
primary_location.source.host_organization_name
primary_location.source.host_organization_lineage	https://openalex.org/P4310310987
primary_location.source.host_organization_lineage_names	Multidisciplinary Digital Publishing Institute
primary_location.license	cc-by
primary_location.pdf_url	https://www.preprints.org/frontend/manuscript/e9f636e1fe05510dc6a06a52b272e9d3/download_pub
primary_location.version	acceptedVersion
primary_location.raw_type	posted-content
primary_location.license_id	https://openalex.org/licenses/cc-by
primary_location.is_accepted	True
primary_location.is_published	False
primary_location.raw_source_name
primary_location.landing_page_url	https://doi.org/10.20944/preprints202504.0831.v1
publication_date	2025-04-10
publication_year	2025
referenced_works_count	0
abstract_inverted_index.a	27, 42, 56, 86, 96, 132
abstract_inverted_index.at	32
abstract_inverted_index.by	25
abstract_inverted_index.in	17, 121
abstract_inverted_index.of	6, 123
abstract_inverted_index.to	60, 77, 100
abstract_inverted_index.KNN	69
abstract_inverted_index.The	0, 92
abstract_inverted_index.and	3, 51, 73, 82, 105, 127, 134, 143
abstract_inverted_index.are	75
abstract_inverted_index.for	12, 89, 137
abstract_inverted_index.the	46, 113
abstract_inverted_index.AUC.	128
abstract_inverted_index.Data	65
abstract_inverted_index.Loss	98
abstract_inverted_index.This	20, 129
abstract_inverted_index.data	40, 44
abstract_inverted_index.from	41
abstract_inverted_index.high	14
abstract_inverted_index.that	112
abstract_inverted_index.Focal	97
abstract_inverted_index.Using	39
abstract_inverted_index.aimed	31
abstract_inverted_index.broad	144
abstract_inverted_index.class	83, 103
abstract_inverted_index.input	88
abstract_inverted_index.model	30, 90, 116
abstract_inverted_index.study	21, 130
abstract_inverted_index.terms	122
abstract_inverted_index.these	23
abstract_inverted_index.SMOTE,	74
abstract_inverted_index.across	36, 146
abstract_inverted_index.handle	78
abstract_inverted_index.hybrid	28
abstract_inverted_index.models	54, 120
abstract_inverted_index.offers	131
abstract_inverted_index.refine	106
abstract_inverted_index.reveal	111
abstract_inverted_index.robust	87
abstract_inverted_index.tasks.	19
abstract_inverted_index.within	55
abstract_inverted_index.Z-score	71
abstract_inverted_index.address	102
abstract_inverted_index.diverse	7
abstract_inverted_index.dynamic	4
abstract_inverted_index.improve	61
abstract_inverted_index.missing	79
abstract_inverted_index.overall	62
abstract_inverted_index.present	9
abstract_inverted_index.recall,	126
abstract_inverted_index.source,	45
abstract_inverted_index.tackles	22
abstract_inverted_index.values,	80
abstract_inverted_index.Logistic	52
abstract_inverted_index.XGBoost,	50
abstract_inverted_index.accuracy	16
abstract_inverted_index.analyses	110
abstract_inverted_index.approach	47
abstract_inverted_index.datasets	8
abstract_inverted_index.domains.	147
abstract_inverted_index.employed	76
abstract_inverted_index.enhanced	141
abstract_inverted_index.ensemble	29, 58, 93, 115
abstract_inverted_index.ensuring	85
abstract_inverted_index.function	99
abstract_inverted_index.inherent	1
abstract_inverted_index.proposed	114
abstract_inverted_index.reliable	135
abstract_inverted_index.solution	136
abstract_inverted_index.weighted	57
abstract_inverted_index.LightGBM,	49
abstract_inverted_index.accuracy,	124
abstract_inverted_index.accuracy.	64
abstract_inverted_index.achieving	13
abstract_inverted_index.datasets.	38
abstract_inverted_index.enhancing	33
abstract_inverted_index.framework	59, 94
abstract_inverted_index.including	68
abstract_inverted_index.outliers,	81
abstract_inverted_index.training.	91
abstract_inverted_index.versatile	133
abstract_inverted_index.Regression	53
abstract_inverted_index.challenges	11, 24
abstract_inverted_index.complexity	2
abstract_inverted_index.imbalanced	37
abstract_inverted_index.imbalances	104
abstract_inverted_index.individual	119
abstract_inverted_index.integrates	48
abstract_inverted_index.precision,	125
abstract_inverted_index.precision.	108
abstract_inverted_index.prediction	63, 107
abstract_inverted_index.predictive	15, 34
abstract_inverted_index.robustness	142
abstract_inverted_index.Comparative	109
abstract_inverted_index.challenges,	139
abstract_inverted_index.competitive	43
abstract_inverted_index.forecasting	18, 138
abstract_inverted_index.imbalances,	84
abstract_inverted_index.imputation,	70
abstract_inverted_index.outperforms	118
abstract_inverted_index.performance	35
abstract_inverted_index.significant	10
abstract_inverted_index.techniques,	67
abstract_inverted_index.consistently	117
abstract_inverted_index.implementing	26
abstract_inverted_index.incorporates	95
abstract_inverted_index.specifically	101
abstract_inverted_index.applicability	145
abstract_inverted_index.demonstrating	140
abstract_inverted_index.preprocessing	66
abstract_inverted_index.normalization,	72
abstract_inverted_index.characteristics	5
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile.value	0.04926108
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True