Feature and decision level fusion using multiple kernel learning and fuzzy integrals

Pinar, Anthony J.; Havens, Timothy C.; Anderson, Derek T.; Hu, Lequn

doi:10.1109/fuzz-ieee.2015.7337934

Cited by 21 publications

(23 citation statements)

References 41 publications

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“…subject to ||w|| p ≤ 1 and w m ∈ R + , where ||w|| p is the p -norm of w. Though the above expres-sion is notationally for M kernels on the same set of features, it is trivially generalized to multiple features, e.g., different kernels on different subsets of features. 29 Optimization-based MKL solutions, versus fixed rule or heuristic approaches, optimize (using alternating optimization typically) the weights of the kernels and the SVM criteria function. Again, we use p -norm MKL 18,19 to derive the LCS weights.…”

Section: Feature Space Fusion Using P -Norm Mklmentioning

confidence: 99%

Fusion of heterogeneous bands and kernels in hyperspectral image processing

Islam

Anderson

Ball

et al. 2019

J. Appl. Rem. Sens.

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Hyperspectral imaging is a powerful technology that is plagued by large dimensionality. Herein, we explore a way to combat that hindrance via non-contiguous and contiguous (simpler to realize sensor) band grouping for dimensionality reduction. Our approach is different in the respect that it is flexible and it follows a well-studied process of visual clustering in high-dimensional spaces. Specifically, we extend the improved visual assessment of cluster tendency and clustering in ordered dissimilarity data unsupervised clustering algorithms for supervised hyperspectral learning. In addition, we propose a way to extract diverse features via the use of different proximity metrics (ways to measure the similarity between bands) and kernel functions. The discovered features are fused with l ∞ -norm multiple kernel learning. Experiments are conducted on two benchmark datasets and our results are compared to related work. These datasets indicate that contiguous or not is application specific, but heterogeneous features and kernels usually lead to performance gain.Hyperspectral imaging is a demonstrated tool for numerous earth and space-borne applications involving target detection, 1-3 invasive species monitoring, 4, 5 and precision agriculture. 6, 7 However, the field suffers from the "curse of dimensionality" (spatial, spectral and temporal). Of interest is new theory for dimensionality reduction or identification of fewer spectral bands for purposes like multispectral vs hyperspectral imaging, typically relative to some task, which ultimately aids efficient computation, storage, transmission, classification and lower system cost. While numerous noteworthy methods have been explored, effectiveness and efficiency of search, fusion and classification remain unsolved.

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Section: Feature Space Fusion Using P -Norm Mklmentioning

confidence: 99%

Fusion of heterogeneous bands and kernels in hyperspectral image processing

Islam

Anderson

Ball

et al. 2019

J. Appl. Rem. Sens.

Self Cite

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“…Another approach to obtain ensemble classification is the use of the Fuzzy Integral (FI) aggregation defined with respect to a Fuzzy Measure (FM) [4][5][6][7][8]. The FI is a non-linear aggregation operator to fuse weighted information from multiple sources, where the weights are captured by a FM.…”

Section: Introductionmentioning

confidence: 99%

“…These works further concluded that DeFIMKL was the best among decision level fusion based FI-FM based ensemble classifiers and thus it has been selected in the work as representative of the FI-FM based ensemble classifier family. However, no in-depth comparison of the FI-FM based ensemble classifier with other ensemble methods have been found in the literature [4][5][6][7][8][9] (discussed in detail in section 2.3). Thus, the motivation of this study is to determine the performance of FM-FI based ensemble methods for the purpose of ensemble classification.…”

Section: Introductionmentioning

confidence: 99%

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Medina¹

2018

Communications in Computer and Information Science

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The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study.

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“…Pinar and colleagues [4][5][6][7][8] built upon the previous works on data-driven FMs and proposed Decision-level Fuzzy Integral Multiple Kernel Learning (De-FIMKL) algorithm as an alternative to algorithmic and algorithm-optimisation hybrid FMs, which aggregates the outputs of SVM classifiers through the use of CFI with respect to a FM learned through a regularised quadratic programming approach. DeFIMKL was compared to FIGA, MKGL and other FI-FM based ensemble classifiers for six datasets.…”

Section: Related Workmentioning

confidence: 99%

“…These ensemble methods have been very popular in the machine learning community due to their ability of producing more accurate results than individual classifiers [2] in a very wide range of application areas [3]. Another approach to obtain ensemble classification is the use of the Fuzzy Integral (FI) aggregation defined with respect to a Fuzzy Measure (FM) [4][5][6][7][8]. The FI is a non-linear aggregation operator to fuse weighted information from multiple sources, where the weights are captured by a FM.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Fuzzy Integral-Fuzzy Measure Based Ensemble Algorithms with the State-of-the-Art Ensemble Algorithms

Agrawal

Pinar

Wagner

et al. 2018

Communications in Computer and Information Science

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Abstract. The Fuzzy Integral (FI) is a non-linear aggregation operator which enables the fusion of information from multiple sources in respect to a Fuzzy Measure (FM) which captures the worth of both the individual sources and all their possible combinations. Based on the expected potential of non-linear aggregation offered by the FI, its application to decision-level fusion in ensemble classifiers, i.e. to fuse multiple classifiers outputs towards one superior decision level output, has recently been explored. A key example of such a FI-FM ensemble classification method is the Decision-level Fuzzy Integral Multiple Kernel Learning (DeFIMKL) algorithm, which aggregates the outputs of kernel based classifiers through the use of the Choquet FI with respect to a FM learned through a regularised quadratic programming approach. While the approach has been validated against a number of classifiers based on multiple kernel learning, it has thus far not been compared to the state-of-the-art in ensemble classification. Thus, this paper puts forward a detailed comparison of FI-FM based ensemble methods, specifically the DeFIMKL algorithm, with state-of-the art ensemble methods including Adaboost, Bagging, Random Forest and Majority Voting over 20 public datasets from the UCI machine learning repository. The results on the selected datasets suggest that the FI based ensemble classifier performs both well and efficiently, indicating that it is a viable alternative when selecting ensemble classifiers and indicating that the non-linear fusion of decision level outputs offered by the FI provides expected potential and warrants further study.

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Feature and decision level fusion using multiple kernel learning and fuzzy integrals

Abstract: Some chapters of this dissertation contain published material. The following list indicates which publications were used along with notes on author contributions.

Cited by 21 publications

References 41 publications

Fusion of heterogeneous bands and kernels in hyperspectral image processing

Fusion of heterogeneous bands and kernels in hyperspectral image processing

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Comparison of Fuzzy Integral-Fuzzy Measure Based Ensemble Algorithms with the State-of-the-Art Ensemble Algorithms

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