A Comparative Study on Recent Automatic Data Fusion Methods
Automatic data fusion is an important field of machine learning that has been increasingly studied. The objective is to improve the classification performance from several individual classifiers in terms of accuracy and stability of the results. This paper presents a comparative study on recent data...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/1/13 |
| _version_ | 1797344353293697024 |
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| author | Luis Manuel Pereira Addisson Salazar Luis Vergara |
| author_facet | Luis Manuel Pereira Addisson Salazar Luis Vergara |
| author_sort | Luis Manuel Pereira |
| collection | DOAJ |
| description | Automatic data fusion is an important field of machine learning that has been increasingly studied. The objective is to improve the classification performance from several individual classifiers in terms of accuracy and stability of the results. This paper presents a comparative study on recent data fusion methods. The fusion step can be applied at early and/or late stages of the classification procedure. Early fusion consists of combining features from different sources or domains to form the observation vector before the training of the individual classifiers. On the contrary, late fusion consists of combining the results from the individual classifiers after the testing stage. Late fusion has two setups, combination of the posterior probabilities (scores), which is called soft fusion, and combination of the decisions, which is called hard fusion. A theoretical analysis of the conditions for applying the three kinds of fusion (early, late, and late hard) is introduced. Thus, we propose a comparative analysis with different schemes of fusion, including weaknesses and strengths of the state-of-the-art methods studied from the following perspectives: sensors, features, scores, and decisions. |
| first_indexed | 2024-03-08T11:01:08Z |
| format | Article |
| id | doaj.art-5a45efd23bcf40f5a72270b826d0ade1 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-08T11:01:08Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-5a45efd23bcf40f5a72270b826d0ade12024-01-26T15:52:35ZengMDPI AGComputers2073-431X2023-12-011311310.3390/computers13010013A Comparative Study on Recent Automatic Data Fusion MethodsLuis Manuel Pereira0Addisson Salazar1Luis Vergara2Instituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, 46022 Valencia, SpainInstituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, 46022 Valencia, SpainInstituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, 46022 Valencia, SpainAutomatic data fusion is an important field of machine learning that has been increasingly studied. The objective is to improve the classification performance from several individual classifiers in terms of accuracy and stability of the results. This paper presents a comparative study on recent data fusion methods. The fusion step can be applied at early and/or late stages of the classification procedure. Early fusion consists of combining features from different sources or domains to form the observation vector before the training of the individual classifiers. On the contrary, late fusion consists of combining the results from the individual classifiers after the testing stage. Late fusion has two setups, combination of the posterior probabilities (scores), which is called soft fusion, and combination of the decisions, which is called hard fusion. A theoretical analysis of the conditions for applying the three kinds of fusion (early, late, and late hard) is introduced. Thus, we propose a comparative analysis with different schemes of fusion, including weaknesses and strengths of the state-of-the-art methods studied from the following perspectives: sensors, features, scores, and decisions.https://www.mdpi.com/2073-431X/13/1/13data fusionearly fusionlate fusionlate hard fusiondecision fusion |
| spellingShingle | Luis Manuel Pereira Addisson Salazar Luis Vergara A Comparative Study on Recent Automatic Data Fusion Methods Computers data fusion early fusion late fusion late hard fusion decision fusion |
| title | A Comparative Study on Recent Automatic Data Fusion Methods |
| title_full | A Comparative Study on Recent Automatic Data Fusion Methods |
| title_fullStr | A Comparative Study on Recent Automatic Data Fusion Methods |
| title_full_unstemmed | A Comparative Study on Recent Automatic Data Fusion Methods |
| title_short | A Comparative Study on Recent Automatic Data Fusion Methods |
| title_sort | comparative study on recent automatic data fusion methods |
| topic | data fusion early fusion late fusion late hard fusion decision fusion |
| url | https://www.mdpi.com/2073-431X/13/1/13 |
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