Estimation and Decision Fusion: A Survey

Sinha, Abhijit; Chen, Huimin; Danu, Daniel; Kirubarajan, T.; Farooq, M.

doi:10.1109/iceis.2006.1703130

Cited by 16 publications

(13 citation statements)

References 36 publications

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“…In order to combine various models to obtain a better prediction, we use confidence-based decision-level method, which is similar to decision score fusion to generate our final results [13]. The confidence score from the time-series model is obtained from softmax layer, and the estimated probabilities from the SVM classifications of the static model is used as the confidence score.…”

Section: Decision Score Fusionmentioning

confidence: 99%

Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

Su¹,

Yeh²,

Ko³

et al. 2018

Interspeech 2018

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In this study, we present a computational framework to participate in the Self-Assessed Affect Sub-Challenge in the INTER-SPEECH 2018 Computation Paralinguistics Challenge. The goal of this sub-challenge is to classify the valence scores given by the speaker themselves into three different levels, i.e., low, medium, and high. We explore fusion of Bi-directional LSTM with baseline SVM models to improve the recognition accuracy. In specifics, we extract frame-level acoustic LLDs as input to the BLSTM with a modified attention mechanism, and separate SVMs are trained using the standard ComParE 16 baseline feature sets with minority class upsampling. These diverse prediction results are then further fused using a decision-level score fusion scheme to integrate all of the developed models. Our proposed approach achieves a 62.94% and 67.04% unweighted average recall (UAR), which is an 6.24% and 1.04% absolute improvement over the best baseline provided by the challenge organizer. We further provide a detailed comparison analysis between different models.

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Section: Decision Score Fusionmentioning

confidence: 99%

Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

Su¹,

Yeh²,

Ko³

et al. 2018

Interspeech 2018

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“…It was also used in the system along with PLS regression for decision fusion. The decision fusion stage aims to combine multiple decisions into a single and consensus one [25]. The linear opinion pool method is used in this case due to its simplicity [3], and a weighted sum rule is defined to combine the predicted values from each decision as in [26].…”

Section: Regressionmentioning

confidence: 99%

Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression

Jan

Meng

Gaus

et al. 2014

Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge

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Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behavior, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and experimental results demonstrate its effectiveness.

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“…The generative ensembles employ the resampling or filtering techniques to boost the training data with different underlying distributions. On the other hand, the nongenerative ensembles combine the CNNs trained from the same data set by using appropriate decision fusion strategies [15,19] or combination rules [12,14,20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

Luo

Zheng

et al. 2014

Mathematical Problems in Engineering

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This paper presents a novel adaptive linear and normalized combination (ALNC) method that can be used to combine the component radial basis function networks (RBFNs) to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error) and the better fidelity (characterized by normalized correlation coefficient) of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

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Estimation and Decision Fusion: A Survey

Cited by 16 publications

References 36 publications

Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression

Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

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