Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method

Mendis,; Gedeon,; Koczy,

doi:10.1109/his.2006.264917

Cited by 13 publications

(12 citation statements)

References 4 publications

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“…However, the choice of the membership function, the aggregation function, and the number of fuzzy sets are critical for getting accurate results. In this study, we adopted the HFS construction approach based on the Levenberg-Marquardt method ( [23]). The fuzzy signature was constructed using the 16 means of each subject's GMM model mentioned earlier as the branches.…”

Section: Hierarchical Fuzzy Signaturementioning

confidence: 99%

A comparative study of different classifiers for detecting depression from spontaneous speech

Alghowinem

Goecke

Wagner

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Accurate detection of depression from spontaneous speech could lead to an objective diagnostic aid to assist clinicians to better diagnose depression. Little thought has been given so far to which classifier performs best for this task. In this study, using a 60-subject real-world clinically validated dataset, we compare three popular classifiers from the affective computing literature -Gaussian Mixture Models (GMM), Support Vector Machines (SVM) and Multilayer Perceptron neural networks (MLP) -as well as the recently proposed Hierarchical Fuzzy Signature (HFS) classifier. Among these, a hybrid classifier using GMM models and SVM gave the best overall classification results. Comparing feature, score, and decision fusion, score fusion performed better for GMM, HFS and MLP, while decision fusion worked best for SVM (both for raw data and GMM models). Feature fusion performed worse than other fusion methods in this study. We found that loudness, root mean square, and intensity were the voice features that performed best to detect depression in this dataset.

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Section: Hierarchical Fuzzy Signaturementioning

confidence: 99%

A comparative study of different classifiers for detecting depression from spontaneous speech

Alghowinem

Goecke

Wagner

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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show abstract

“…The two equations (10) and (11) above, together with the chain rule for derivation have been used to calculate the Jacobian, which is used to approximate the Hessian matrix of the LM learning. A detailed discussion of the method of using LM for learning WRAO can be found in [22].…”

Section: A Methods Of Extracting Wrao From Datamentioning

confidence: 99%

“…We learnt the WRAOs for each node in the High Salary Selection fuzzy signature structure in figure 6 automatically [22]. Figures 7 and 8 show training and test results of the experiment.…”

Section: W111mentioning

confidence: 99%

Polymorphic fuzzy signatures

Mendis

Gedeon

2010

International Conference on Fuzzy Systems

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The fuzzy signature [1], [2] approach is aimed at finding a hierarchically decomposed solutions by adding new elements to Zadeh's approach [3]. It tackles the problem by splitting the problem into hierarchically organized local sub-models and by applying more complex and heterogenous descriptors, more fit for the identification of extremely complex models. However, the computational time complexity still affects the fuzzy signatures as we were attempt to create an atomic fuzzy signature for each data point we get. Importantly, the atomic fuzzy signatures we store has properties we can make use of to make search over this structure computationally efficient. In this paper we introduce a new approach that uses the metadata about a set of fuzzy signatures to extract a Polymorphic Fuzzy Signature. Productively, a polymorphic fuzzy signature represents its base set of fuzzy signatures in a higher meta level which also allows search/inference, and so can reduce the computational time complexity of the inference process.

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“…We use our LM based method for learning [27,28] of both weights and the parameter p in GOWA operators. First, to avoid the 2 constraints on the weights w i in Definition 3, we replaced it by the following function [10],…”

Section: Aggregation and Weights Learning Of Gowamentioning

confidence: 99%

WRAO and OWA learning using Levenberg–Marquardt and genetic algorithms

Mendis

Gedeon

2010

Memetic Comp.

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The generalized Weighted Relevance Aggregation Operator (WRAO) is a non-additive aggregation function. The Ordered Weighted Aggregation Operator (OWA) (or its generalized form: Generalized Ordered Weighted Aggregation Operator (GOWA)) is more restricted with the additivity constraint in its weights. In addition, it has an extra weights reordering step making it hard to learn automatically from data. Our intension here is to compare the efficiency (or effectiveness) of learning these two types of aggregation functions from empirical data. We employed two methods to learn WRAO and GOWA: Levenberg-Marquardt (LM) and a Genetic Algorithm (GA) based method. We use UCI (University of California Irvine) benchmark data to compare the aggregation performance of non-additive WRAO and additive GOWA. We found that the non-constrained aggregation function WRAO was learnt well automatically and produced consistent results, while GOWA was learnt less well and quite inconsistently.

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Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method

Cited by 13 publications

References 4 publications

A comparative study of different classifiers for detecting depression from spontaneous speech

A comparative study of different classifiers for detecting depression from spontaneous speech

Polymorphic fuzzy signatures

WRAO and OWA learning using Levenberg–Marquardt and genetic algorithms

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