Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

García-Domínguez, Antonio; Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Gamboa-Rosales, Hamurabi; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Luna-García, Huizilopoztli; Magallanes‐Quintanar, Rafael

doi:10.1155/2020/8617430

Cited by 22 publications

(19 citation statements)

References 46 publications

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“…For feature selection through genetic algorithms, we used the genetic Galgo algorithm [37] and the random forest classifier algorithm [38], which demonstrated superior accuracy to the algorithms k-nearest neighbor, nearest centroid, artificial neural networks and recursive partition trees in our previous work of feature selection, using genetic algorithms applied to children's activity classification using environmental sound [17].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

et al. 2021

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Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

et al. 2021

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“…Dominguez et al [33] utilize GA for feature selection to reduce the original size of the environmental sound data. Liu et al [34] propose a fast wrapper feature subset selection algorithm based on PSO, which employs the domain knowledge of feature subset selection problems.…”

Section: Related Workmentioning

confidence: 99%

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Kang

Sun

et al. 2020

IEEE Access

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Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

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“…Genetic algorithms (GAs) have been previously used for feature selection and showed significant results for selecting the best feature sets [24,25]. In the health arena, GA can highly improve the performance of models for emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activity recognition and classification [28], gene encoder [29], hepatitis prediction [30], and COVID-19 patient detection [31].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Feature selection is used to reduce the dimensionality of data [20,21] and in medical diagnosis, is used to identify most significant features related to disease [22,23]. Genetic algorithm (GA) is a feature selection method that has been used to find the best feature subsets [24,25] and shown significant advantage for improving the performance of emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activities regarding recognition and classification [28], gene encoder [29], hepatitis prediction [30], and coronavirus disease (COVID-19) patient detection [31]. A previous study revealed that a combination of GA and XGBoost has improved the model performance [32].…”

Section: Introductionmentioning

confidence: 99%

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

Cited by 22 publications

References 46 publications

Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

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