A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors

Mohíno-Herranz, Inma; Gil-Pita, Roberto; García-Gómez, Joaquín; Rosa-Zurera, Manuel; Seoane, Fernando

doi:10.3390/s20010309

Cited by 10 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to verify the effectiveness of the feature selection method based on counterfactual explanations, we compare it with the method without feature selection and the wrapper method [63] , where the feature selection is performed on the 20 artificial statistical features from the six open-source projects mentioned in Section 4.1.…”

Section: Baselinesmentioning

confidence: 99%

Software defect prediction based on counterfactual explanations

Zheng

Chen

et al. 2022

Preprint

View full text Add to dashboard Cite

Software defect prediction is critical to ensure the software quality. Researchers have worked on building various defect prediction models to improve the performance of defect prediction. The existing defect prediction models are mainly divided into two categories: one is the models constructed based on the artificial statistical features and the other is the models constructed based on the semantic features. DP-CNN [29] is one of the best defect prediction models, because it combines both the artificial statistical features and semantic features, so its performance is greatly improved compared with traditional defect prediction models. In this paper, we are inspired by the DP-CNN model and make the following two improvements: firstly, we use a new network representation technique Struc2vec to mine the information existing between software modules, which specializes in learning node representations from structural identity and can further extract the structural features associated with defects. We let the DP-CNN model again incorporate the newly mined structural features. Then, we propose a feature selection method based on counterfactual explanations which preserves reasonable causal relationships between features and thus produces a better subset of features compared to traditional feature selection methods. We used it to optimize the artificial statistical features inside the DP-CNN model. Based on the above methods, we propose a new hybrid defect prediction model DPS-CNN-STR. We evaluate our model on six open-source projects in terms of F1-score in defect prediction. The experimental results show that DPS-CNN-STR improves the state-of-the-art method by 3.6% on average.

show abstract

Section: Baselinesmentioning

confidence: 99%

Software defect prediction based on counterfactual explanations

Zheng

Chen

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is worth noting that emotional expression is a diversified process, and research on single modality is not enough. With the improvement of multi-source heterogeneous information fusion theory, the complementation and promotion of multi-modality information can make up for the defects of single modality [10] . Therefore, multi-modality emotion recognition based on multi-type sensor fusion has gradually become a research hotspot [11] .…”

Section: Introductionmentioning

confidence: 99%

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Zhao,

Guo,

Chen

et al. 2024

Big Data Min. Anal.

View full text Add to dashboard Cite

Walking as a unique biometric tool conveys important information for emotion recognition. Individuals in different emotional states exhibit distinct walking patterns. For this purpose, this paper proposes a novel approach to recognizing emotion during walking using electroencephalogram (EEG) and inertial signals.Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion. Subjects wear virtual reality head-mounted display (VR-HMD) equipment to immerse in strong emotions during walking. VR environment shows excellent imitation and experience ability, which plays an important role in awakening and changing emotions. In addition, the multi-modal signals acquired from EEG and inertial sensors are separately represented as virtual emotion images by discrete wavelet transform (DWT). These serve as input to the attention-based convolutional neural network (CNN) fusion model. The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features. To effectively improve the performance of the recognition system, the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results. An investigation is made on the effect of diverse mother wavelet types and wavelet decomposition levels on model performance which indicates that the 2.2-order reverse biorthogonal (rbio2.2) wavelet with two-level decomposition has the best recognition performance.Comparative experiment results show that the proposed method outperforms other existing state-of-the-art works with an accuracy of 98.73%.

show abstract

Machine Learning‐Enhanced Triboelectric Sensing Application

Shang,

Chen,

Cai

et al. 2024

Adv Materials Technologies

View full text Add to dashboard Cite

Triboelectric nanogenerator (TENG) has become a promising candidate for wearable energy harvesting and self‐powered sensing systems. However, processing large amounts of data imposes a computing power barrier for practical application. Machine learning‐assisted self‐powered sensors based on TENG have been widely used in data‐driven applications due to their excellent characteristics such as no additional power supply, high sensing accuracy, low cost, and good biocompatibility. This work comprehensively reviews the latest progress in machine learning (ML)‐assisted TENG‐based sensors. The future challenges and opportunities are discussed. First, the fundamental principles including the working mode of ML‐assisted TENG‐based sensor and common algorithms are systematically and comprehensively illustrated, which emphasizes the algorithm definition and principle. Subsequently, the progress of ML methods in the field of TENG‐based sensors is further reviewed, summarizing the advantages and disadvantages of various algorithms in practical examples, and providing guidance and suggestions on how to choose the appropriate methods. Finally, the prospects and challenges of ML‐assisted TENG‐based sensors is summarized. Directions and important insights for the future development of TENG and AI integration is provided.

show abstract

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors

Cited by 10 publications

References 36 publications

Software defect prediction based on counterfactual explanations

Software defect prediction based on counterfactual explanations

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Machine Learning‐Enhanced Triboelectric Sensing Application

Contact Info

Product

Resources

About