GA for feature selection of EEG heterogeneous data

• URL: http://arxiv.org/abs/2103.07117v1
• Date: Fri, 12 Mar 2021 07:27:42 GMT
• Title: GA for feature selection of EEG heterogeneous data
• Authors: Aurora Saibene (1 and 2) and Francesca Gasparini (1 and 2) ((1) University of Milano-Bicocca, Department of Informatics, Systems and Communications, Multi Media Signal Processing Laboratory, (2) University of Milano-Bicocca, NeuroMI)
• Abstract summary: We propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. The proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.

Related papers

• Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging [0.6216545676226375]
  EEG signals play a significant role in sleep research. Due to the high-dimensional nature of EEG signal data sequences, data visualization and clustering of different sleep stages have been challenges. We propose a two-stage hierarchical and explainable feature selection framework by incorporating a feature selection algorithm to improve the performance of dimensionality reduction.
  arXiv  Detail & Related papers  (2024-08-31T23:54:53Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies [50.11449968854487]
  Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits. Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS. We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
  arXiv  Detail & Related papers  (2023-09-26T03:59:21Z)
• Distillation-guided Representation Learning for Unconstrained Gait Recognition [50.0533243584942]
  We propose a framework, termed GAit DEtection and Recognition (GADER), for human authentication in challenging outdoor scenarios. GADER builds discriminative features through a novel gait recognition method, where only frames containing gait information are used. We evaluate our method on multiple State-of-The-Arts(SoTA) gait baselines and demonstrate consistent improvements on indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2023-07-27T01:53:57Z)
• On the Validation of Gibbs Algorithms: Training Datasets, Test Datasets and their Aggregation [70.540936204654]
  dependence on training data of the Gibbs algorithm (GA) is analytically characterized. This description enables the development of explicit expressions involving the training errors and test errors of GAs trained with different datasets.
  arXiv  Detail & Related papers  (2023-06-21T16:51:50Z)
• Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data [14.086094296850122]
  Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. external noise often obstructs EEG data due to its biological nature and complex data collection process. We propose a novel approach that integrates feature selection and time segmentation of EEG data.
  arXiv  Detail & Related papers  (2023-03-25T23:44:39Z)
• A GA-like Dynamic Probability Method With Mutual Information for Feature Selection [1.290382979353427]
  We propose a GA-like dynamic probability (GADP) method with mutual information. As each gene's probability is independent, the chromosome variety in GADP is more notable than in traditional GA. To verify our method's superiority, we evaluate our method under multiple conditions on 15 datasets.
  arXiv  Detail & Related papers  (2022-10-21T13:30:01Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Explanation-Guided Fairness Testing through Genetic Algorithm [18.642243829461158]
  This work proposes ExpGA, an explanationguided fairness testing approach through a genetic algorithm (GA) ExpGA employs the explanation results generated by interpretable methods to collect high-quality initial seeds. It then adopts GA to search discriminatory sample candidates by optimizing a fitness value.
  arXiv  Detail & Related papers  (2022-05-16T02:40:48Z)
• An Evolutionary Correlation-aware Feature Selection Method for Classification Problems [3.2550305883611244]
  In this paper, an estimation of distribution algorithm is proposed to meet three goals. Firstly, as an extension of EDA, the proposed method generates only two individuals in each iteration that compete based on a fitness function. Secondly, we provide a guiding technique for determining the number of features for individuals in each iteration. As the main contribution of the paper, in addition to considering the importance of each feature alone, the proposed method can consider the interaction between features.
  arXiv  Detail & Related papers  (2021-10-16T20:20:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.