A Theoretical Study on Six Classifier Fusion Strategies

By Ludmila I. Kuncheva. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

801 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/92e9dd55ff5f66f20fb9cc24d8baa1cf7bea83b6

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Citations in the corpus

Spoken Language Recognition: From Fundamentals to Practice (2013, background): “It is generally believed that the major factor for attaining classifier fusion with a better accuracy is the diversity in the subsystems [61].”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “The approaches used most often include the majority vote, averaging, weighted averaging, the Bayesian approach, the fuzzy integral, the DempsterShafer theory, the Borda count, aggregation through order statistics, probabilistic aggregation, the fuzzy templates, and stacked generalization (Kuncheva e”
Bi-modal emotion recognition from expressive face and body gestures (2007, neutral): “[15].”
Classifier ensembles: Select real-world applications (2008, background): “6 Examples of work in this area include [66,112,40].”
A Dataset for Breast Cancer Histopathological Image Classification (2016, background): “The oracle is an abstract model defined in [15], which always selects the classifier that predicted the correct label, for a given query sample, if such a classifier exists.”
Survey on speech emotion recognition: Features, classification schemes, and databases (2011, methodology): “In fact, MCS is now a well-established area in pattern recognition [66,67,127,126] and there are many aggregation techniques that have not been applied to speech emotion recognition such as Adaboost.”
Ensemble based systems in decision making (2006, background): “Does the no-free-lunch theorem hold for combination rules as well? Many studies have been conducted to answer this question, including [18], [27], [28], [31], [36], [37], [42], [71], [72], among others.”
The choice of scaling technique matters for classification performance (2022, methodology): “[34] that is inspired by the Oracle concept [35].”
A theoretical and experimental analysis of linear combiners for multiple classifier systems (2005, background): “Of the various combining rules proposed in the literature, linear combiners are the most frequently used [22], [12], [2], [30], [16], [31], [32], [28], [20], [19].”