A novel extreme learning machine based kNN classification method for dealing with big data

Shokrzade, Amin; Ramezani, Mohsen; Tab, Fardin Akhlaghian; Mohammad, Mahmud Abdulla

doi:10.1016/j.eswa.2021.115293

Cited by 79 publications

(26 citation statements)

References 44 publications

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“…The k-NN algorithm assumes that related things are located nearby. Therefore, the success of the classification depends heavily on the value of k and the distance metric, which must be chosen before using k-NN [ 41 ]. Here, we have used a grid search algorithm to obtain the optimal hyper-parameters, where the classification accuracy was used as the optimization metric.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Hashem

AbdulAzeem

Labib

et al. 2023

Sensors

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Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model’s predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life.

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Section: Methodsmentioning

confidence: 99%

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Hashem

AbdulAzeem

Labib

et al. 2023

Sensors

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“…This algorithm is one of the simplest, but still one of the most efficient classification algorithms. The basic idea of this algorithm is that similar objects are found at a very short distance from each other 19 .…”

Section: Methodsmentioning

confidence: 99%

Automated detection of celiac disease using Machine Learning Algorithms

Stoleru

Dulf

Ciobanu

2022

Sci Rep

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Celiac disease is a disorder of the immune system that mainly affects the small intestine but can also affect the skeletal system. The diagnosis relies on histological assessment of duodenal biopsies acquired by upper digestive endoscopy. Immunological tests involve collecting a blood sample to detect if the antibodies have been produced in the body. Endoscopy is invasive and histology is time-consuming. In recent years there have been various algorithms that use artificial intelligence (AI) and neural convolutions (CNN, Convolutional Neural Network) to process images from capsule endoscopy, a non-invasive endoscopy approach, that provides magnified, high qualitative images of the small bowel mucosa, to quickly establish a diagnosis. The proposed innovative approach do not use complex learning algorithms, instead it find some artefacts in the endoscopies using kernels and use classified machine learning algorithms. Each used artefacts have a psychical meaning: atrophies of the mucosa with a visible submucosal vascular pattern; the presence of cracks (depressions) that have an appearance similar to that of dry land; reduction or complete loss of folds in the duodenum; the presence of a submerged appearance at the Kerckring folds and a low number of villi. The results obtained for video capsule endoscopy images processing reveal an accuracy of 94.1% and F1 score of 94%, which is competitive with other complex algorithms. The main goal of the present research was to demonstrate that computer-aided diagnosis of celiac disease is possible even without the use of very complex algorithms, which require expensive hardware and a lot of processing time. The use of the proposed automated images processing acquired noninvasively by capsule endoscopy would be assistive in detecting the subtle presence of villous atrophy not evident by visual inspection. It may also be useful to assess the degree of improvement of celiac. Patients on a gluten-free diet, the main treatment method for stopping the autoimmune process and improving the state of the small intestinal villi. The novelty of the work is that the algorithm uses two modified filters to properly analyse the intestine wall texture. It is proved that using the right filters, the proper diagnostic can be obtained by image processing, without the use of a complicated machine learning algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Since the selection and trade-off of the window function is an obstacle to the application of the Parzen algorithm, while the KNN algorithm is based on the posterior probability of the sequence element distance or similarity measure, which is more generalizable. [13][14][15] The principle of the prediction algorithm based on KNN estimation is to construct a certain segment vector sequence, find K neighbors by the method of distance or similarity measurement, and obtain a new prediction value through a certain average or weighting process. Due to the simple and intuitive algorithm of non-parametric prediction methods, when the sample value is large enough, its error is low, and the prediction effect under special (fault or singular point) conditions is more accurate than parametric modeling.…”

Section: Introductionmentioning

confidence: 99%

Research on non-parametric prediction method of reciprocating compressor time series based on prediction credibility scale

Pan

Liu

et al. 2022

Advances in Mechanical Engineering

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Aiming at the long-term unpredictability of the reciprocating compressor vibration signal, a non-parametric prediction method of reciprocating compressor time series based on the prediction credibility scale is proposed in this paper. The method is to take the multifractal singular spectrum as the prediction parameter and use the Smoothness Priors Approach (SPA) method to obtain the singular spectrum parameters of different components, and construct the phase space reconstruction dynamic modeling domains. It enables the prediction model to reflect the real-time characteristics of the dynamics evolution of complex systems and highlights the independent influence of each component on the prediction. Meanwhile, the information entropy saturation principle is introduced into the K-Nearest Neighbor (KNN) model to establish the improved K neighborhood dynamic non-parametric prediction model based on the maximum prediction credibility scale, which improves the credibility of the prediction results. Finally, a complete SPA&PSR_KNN prediction algorithm is proposed. Through example validation and error analysis, compared with KNN, BP, and SVM, it can be seen that the prediction results of spectral characteristic parameters obtained by this algorithm have smaller error and higher reliability, and faster operation speed. Thus, the prediction of vibration signal time series of reciprocating compressor is realized.

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A novel extreme learning machine based kNN classification method for dealing with big data

Cited by 79 publications

References 44 publications

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model

Automated detection of celiac disease using Machine Learning Algorithms

Research on non-parametric prediction method of reciprocating compressor time series based on prediction credibility scale

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