Feature Selection with Artificial Bee Colony Algorithms for Classifying Parkinson’s Diseases

Durgut, Rafet; Baydilli, Yusuf Yargı; Aydın, Mehmet Emin

doi:10.1007/978-3-030-48791-1_26

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…Many studies have used publicly available datasets from different repositories for PD diagnosis employing NI algorithm for feature selection and ML algorithms for classification. In this research study, the data set is collected from UCI ML Repository [65] which was also used in some previous studies mentioned in the literature section [43], [63]. The dataset is prepared from 252 patients whose speeches were recorded at the Department of Neurology at Istanbul University, where 188 (107 men and 81 women) were PD patients from the age 33 to 87 and 64 (23 men and 41 women) were healthy individuals from the age 41 to 82.…”

Section: Methodology a Datasetmentioning

confidence: 99%

“…In addition, with OCFA, this study included MGWO for performance comparison, where the proposed MGWO selected the lowest number of features and highest accuracy. Later in the same year, Durgut et al [63] used binary versions of the artificial bee colony algorithm to reduce irrelevant features and deployed KNN for classification and SVM in the second stage to compare individual results. Dash et al [64] employed a chaotic firefly algorithm integrated with a kernel-based NB algorithm for discriminant features, and five classifiers were brought for classification.…”

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confidence: 99%

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Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis

et al. 2023

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Background:Parkinson's disease (PD) is a prominent neurodegenerative disease that damages the neurons of the substantia nigra, causing irreversible impairments leading to involuntary movements. As this disease disrupts patients' daily activities in a mature stage, early detection of the disease is crucial. Several methods based on nature-inspired (NI) algorithms have been proposed for PD detection and patient management. As there are several NI algorithms for feature selection, a mapping with an individual machine learning (ML) classifier is necessary to obtain optimal performance of the detection pipeline. Method:To fill this gap, in this work, 13 NI algorithms and 11 ML classifiers were selected, and critical comparisons were performed regarding their combined performance in detecting PD. Each NI algorithm was employed to select an optimal feature set which was then classified by the 11 ML classifiers keeping the same parameters. This generated 143 NI-ML pairs, which were carefully compared to find the best-performing pairs considering several assessment criteria such as accuracy, cross-validation mean score, precision, recall and F1-score. Results:The results of the extensive comparative analysis allowed the ranking of the algorithms in the 50th, 75th and 95th percentile to identify the best-performing pairs. The analyses revealed that 12 NI-ML models obtained a testing accuracy of over 91%, which is above the 95th percentile value. The Flower Pollination Algorithm and Extreme Gradient Boost Algorithm pair obtained the highest testing accuracy of 93%. Conclusion:This study revealed the remarkable performance of the boosting algorithms promoting explainable machine learning in PD detection.

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Section: Methodology a Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis

et al. 2023

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“…Attempts to characterise such search spaces faces increasing the computational complexity of most learning algorithms -for which the number of input features and sample size are critical parameters. To reduce space and computational complexities, the number of features of a given problem should be reduced (Durgut et al 2020). Many predictors benefit from the feature selection process since it reduces overfitting and improves accuracy, among other things (Chandrashekar and Sahin 2014).…”

Section: Related Workmentioning

confidence: 99%

Feature-based search space characterisation for data-driven adaptive operator selection

Aydin,

Durgut,

Rakib

et al. 2023

Evolving Systems

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Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide the search using domain-knowledge and act accordingly. Many problem solving algorithms use multiple complementary operators in patterns to handle such unpredictable cases. A well-characterised search space may help to evaluate the problem states better and select/apply a neighbourhood operator to generate more productive new problem states that allow for a smoother path to the final/optimum solutions. This applies to the algorithms that use multiple operators to solve problems. However, the remaining challenge is determining how to select an operator in an optimal way from the set of operators while taking the search space conditions into consideration. Recent research shows the success of adaptive operator selection to address this problem. However, efficiency and scalability issues persist in this regard. In addition, selecting the most representative features remains crucial in addressing problem complexity and inducing commonality for transferring experience across domains. This paper investigates if a problem can be represented by a number of features identified by landscape analysis, and whether an adaptive operator selection scheme can be constructed using Machine Learning (ML) techniques to address the efficiency and scalability problem. The proposed method determines the optimal categorisation by analysing the predictivity of a set of features using the most well-known supervised ML techniques. The identified set of features is then used to construct an adaptive operator selection scheme. The findings of the experiments demonstrate that supervised ML algorithms are highly effective when building adaptable operator selectors.

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“…As an alternative to the Euclidean function, Manhattan, Minkowski and Hamming functions can also be used. After the distance is calculated, it is sorted, and the incoming value is assigned to the appropriate class [29].…”

Section: K-nearest Neighbormentioning

confidence: 99%

Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

Başaran

Çelik

2022

Preprint

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If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years many diseases have been rapidly detected with high accuracy with artificial intelligence methods and the treatment process has accelerated. Convolutional neural networks one of the artificial intelligence methods provide very detailed information about images and extremely successful results are obtained in classifying images. In this study firstly the data set was trained with the EfficientNetB0 model which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting with PSO and GA optimization algorithms, and different feature combinations were created. Each of these selected feature sets were classified by the SVM method and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89,17%.

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Feature Selection with Artificial Bee Colony Algorithms for Classifying Parkinson’s Diseases

Cited by 5 publications

References 28 publications

Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis

Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis

Feature-based search space characterisation for data-driven adaptive operator selection

Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

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