Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness

Wutzl, Betty; Leibnitz, Kenji; Rattay, Frank; Kronbichler, Martin; Murata, Masayuki; Golaszewski, Stefan

doi:10.1371/journal.pone.0219683

Cited by 39 publications

(15 citation statements)

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“…Nervous signals will adjust a system’s parameters to optimize performance and furthermore, attempt to induce functional and structural changes in the nervous system, while system parameters will be constantly readjusting based on measurement results. Such a loop would require searching the parameter space with AI algorithms for efficiency, a task at which genetic algorithms have been demonstrated to excel [ 261 , 262 ]. On a hardware level, this procedure may ultimately lead to the development of true “neural dust”, a free floating, swarm-level, AI-based, self-adjustable interface with the nervous system for seamless acquisition and control [ 257 , 263 , 264 ].…”

Section: Discussionmentioning

confidence: 99%

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Nizamis

Athanasiou

Almpani

et al. 2021

Sensors

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Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

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Section: Discussionmentioning

confidence: 99%

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Nizamis

Athanasiou

Almpani

et al. 2021

Sensors

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“…Feature selection (FS) is a process of selecting language discriminating features for improving SLID system in duration mismatched conditions while reducing computational complexity [12,13]. Several FS techniques such as genetic algorithm [14], estimation of distribution algorithm (EDA), and greedy search [15] have been reported in the literature. Chowdhary et al [16], presented a grey wolf optimizer (GWO) FS algorithm for selecting optimum features for improving SLID.…”

Section: Review Of Related Workmentioning

confidence: 99%

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Bakshi

Kopparapu

2021

SN COMPUT. SCI.

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Paper presents novel duration normalized feature selection technique and two-step modified hierarchical classifier to improve the accuracy of spoken language identification (SLID) using Indian languages for duration mismatched condition. Feature selection averages random forest-based importance vectors of open SMILE features of different duration utterances. Although it improves the SLID system's accuracy for mismatched training and testing durations, the performance is significantly reduced for short-duration utterances. A cascade of inter-family and intra-family classifiers with an additional class to improve false language family estimation. All India Radio data set with nine Indian languages and different utterance durations was used as speech material. Experimental results showed that 150 optimal features with the proposed modified hierarchical classifier showed the highest accuracy of 96.9% and 84.4% for 30 s and 0.2 s utterances for the same train-test duration. However, we achieved an accuracy of 98.3% and 61.9% for 15 and 0.2 s test duration when trained with 30 s duration utterance. Comparative analysis showed a significant improvement in accuracy than several SLID systems in the literature.

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“…Our goal is to identify z attributes with the highest predictive accuracy in class imbalanced settings and identify the best features from both minority and majority classes. Because of that, the fitness criterion needs to be formulated to realize these objectives [104]. Each solution is assessed based on the fitness function formulated using the kNN classifier to achieve the highest AUC and identify the reduct features from both the minority and majority classes that best predict the minority class.…”

Section: The Proposed Wrapper Approachmentioning

confidence: 99%

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

Sharifai

Zainol

2021

IEEE Access

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DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

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Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness

Cited by 39 publications

References 50 publications

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

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