ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter Article Swipe
Arjun Pakrashi
,
Brian Mac Namee
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s42979-023-02280-4
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s42979-023-02280-4
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman filter-based Heuristic ensemble (KFHE) is an ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This work proposes a multi-label version of KFHE, ML-KFHE, demonstrating the effectiveness of the KFHE method on multi-label datasets. Two variants are introduced based on the underlying component classifier algorithm, ML-KFHE-HOMER, and ML-KFHE-CC which uses HOMER and classifier chain (CC) as the underlying multi-label algorithms, respectively. ML-KFHE-HOMER and ML-KFHE-CC sequentially train multiple HOMER and CC multi-label classifiers and aggregate their outputs using the sensor fusion properties of the Kalman filter. Extensive experiments and detailed analysis were performed on thirteen multi-label datasets and eight other algorithms, which included state-of-the-art ensemble methods. The results show, for both versions, the ML-KFHE framework improves the predictive performance significantly with respect to bagged combinations of HOMER (named E-HOMER), also introduced in this paper, and bagged combination of CC, ensemble classifier chains (ECC), thus demonstrating the effectiveness of ML-KFHE. Also, the ML-KFHE-HOMER variant was found to perform consistently and significantly better than the compared multi-label methods including existing approaches based on ensembles.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s42979-023-02280-4
- OA Status
- hybrid
- Cited By
- 1
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387959754
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- OpenAlex ID
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https://openalex.org/W4387959754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s42979-023-02280-4Digital Object Identifier
- Title
-
ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman FilterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-10-26Full publication date if available
- Authors
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Arjun Pakrashi, Brian Mac NameeList of authors in order
- Landing page
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https://doi.org/10.1007/s42979-023-02280-4Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1007/s42979-023-02280-4Direct OA link when available
- Concepts
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Computer science, Classifier (UML), Kalman filter, Artificial intelligence, Multi-label classification, Pattern recognition (psychology), Fusion, Algorithm, Ensemble Kalman filter, Ensemble learning, Random subspace method, Machine learning, Extended Kalman filter, Data mining, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 38, 54 |
| abstract_inverted_index.this | 178 |
| abstract_inverted_index.thus | 189 |
| abstract_inverted_index.uses | 96 |
| abstract_inverted_index.very | 60 |
| abstract_inverted_index.were | 137 |
| abstract_inverted_index.with | 166 |
| abstract_inverted_index.work | 63 |
| abstract_inverted_index.Also, | 195 |
| abstract_inverted_index.HOMER | 97, 114, 172 |
| abstract_inverted_index.KFHE, | 69 |
| abstract_inverted_index.based | 13, 85, 215 |
| abstract_inverted_index.chain | 100 |
| abstract_inverted_index.eight | 144 |
| abstract_inverted_index.found | 200 |
| abstract_inverted_index.other | 16, 145 |
| abstract_inverted_index.show, | 154 |
| abstract_inverted_index.shown | 57 |
| abstract_inverted_index.their | 121 |
| abstract_inverted_index.train | 112 |
| abstract_inverted_index.using | 123 |
| abstract_inverted_index.which | 95, 147 |
| abstract_inverted_index.(ECC), | 188 |
| abstract_inverted_index.(KFHE) | 33 |
| abstract_inverted_index.(named | 173 |
| abstract_inverted_index.Kalman | 29, 46, 130 |
| abstract_inverted_index.bagged | 169, 181 |
| abstract_inverted_index.better | 206 |
| abstract_inverted_index.chains | 187 |
| abstract_inverted_index.filter | 47 |
| abstract_inverted_index.fusion | 42, 126 |
| abstract_inverted_index.method | 37, 77 |
| abstract_inverted_index.paper, | 179 |
| abstract_inverted_index.sensor | 41, 125 |
| abstract_inverted_index.widely | 22 |
| abstract_inverted_index.Despite | 0 |
| abstract_inverted_index.ML-KFHE | 159 |
| abstract_inverted_index.bagging | 18 |
| abstract_inverted_index.combine | 49 |
| abstract_inverted_index.filter. | 131 |
| abstract_inverted_index.methods | 6, 12, 211 |
| abstract_inverted_index.models, | 52 |
| abstract_inverted_index.outputs | 122 |
| abstract_inverted_index.perform | 202 |
| abstract_inverted_index.respect | 167 |
| abstract_inverted_index.results | 153 |
| abstract_inverted_index.several | 50 |
| abstract_inverted_index.success | 2 |
| abstract_inverted_index.variant | 198 |
| abstract_inverted_index.version | 67 |
| abstract_inverted_index.ML-KFHE, | 70 |
| abstract_inverted_index.ML-KFHE. | 194 |
| abstract_inverted_index.analysis | 136 |
| abstract_inverted_index.compared | 209 |
| abstract_inverted_index.datasets | 142 |
| abstract_inverted_index.detailed | 135 |
| abstract_inverted_index.ensemble | 4, 11, 32, 36, 150, 185 |
| abstract_inverted_index.existing | 213 |
| abstract_inverted_index.exploits | 39 |
| abstract_inverted_index.explored | 23 |
| abstract_inverted_index.improves | 161 |
| abstract_inverted_index.included | 148 |
| abstract_inverted_index.methods. | 151 |
| abstract_inverted_index.multiple | 113 |
| abstract_inverted_index.proposes | 64 |
| abstract_inverted_index.thirteen | 140 |
| abstract_inverted_index.variants | 82 |
| abstract_inverted_index.E-HOMER), | 174 |
| abstract_inverted_index.Extensive | 132 |
| abstract_inverted_index.Heuristic | 31 |
| abstract_inverted_index.aggregate | 120 |
| abstract_inverted_index.component | 89 |
| abstract_inverted_index.datasets. | 80 |
| abstract_inverted_index.framework | 160 |
| abstract_inverted_index.including | 212 |
| abstract_inverted_index.performed | 138 |
| abstract_inverted_index.problems, | 10 |
| abstract_inverted_index.problems. | 27 |
| abstract_inverted_index.versions, | 157 |
| abstract_inverted_index.ML-KFHE-CC | 94, 110 |
| abstract_inverted_index.algorithm, | 91 |
| abstract_inverted_index.approaches | 15, 214 |
| abstract_inverted_index.classifier | 51, 90, 99, 186 |
| abstract_inverted_index.effective. | 61 |
| abstract_inverted_index.ensembles. | 217 |
| abstract_inverted_index.introduced | 84, 176 |
| abstract_inverted_index.predictive | 163 |
| abstract_inverted_index.properties | 43, 127 |
| abstract_inverted_index.underlying | 88, 104 |
| abstract_inverted_index.algorithms, | 106, 146 |
| abstract_inverted_index.classifiers | 118 |
| abstract_inverted_index.combination | 182 |
| abstract_inverted_index.experiments | 133 |
| abstract_inverted_index.multi-class | 8 |
| abstract_inverted_index.multi-label | 25, 66, 79, 105, 117, 141, 210 |
| abstract_inverted_index.performance | 164 |
| abstract_inverted_index.combinations | 170 |
| abstract_inverted_index.consistently | 203 |
| abstract_inverted_index.filter-based | 30 |
| abstract_inverted_index.sequentially | 111 |
| abstract_inverted_index.ML-KFHE-HOMER | 108, 197 |
| abstract_inverted_index.demonstrating | 71, 190 |
| abstract_inverted_index.effectiveness | 73, 192 |
| abstract_inverted_index.respectively. | 107 |
| abstract_inverted_index.significantly | 165, 205 |
| abstract_inverted_index.ML-KFHE-HOMER, | 92 |
| abstract_inverted_index.classification | 5, 9, 26 |
| abstract_inverted_index.state-of-the-art | 149 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.59374262 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |