A machine learning approach to circumventing the curse of dimensionality in discontinuous time series machine data

Aremu, Oluseun Omotola; Hyland-Wood, David; McĂree, P. R.

doi:10.1016/j.ress.2019.106706

Cited by 52 publications

(26 citation statements)

References 34 publications

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“…A well-known problem with genetics data is that the number of attributes is much larger than the number of samples, introducing a challenge not only for ML algorithms but for DL and hence the general statistical approaches [78]. To avoid the curse of dimensionality, feature selection and feature extraction are often used [79,80]. Some researchers [81][82][83][84] try using multiple datasets to provide a larger number of samples to balance the number of features and samples.…”

Section: A the Curse Of Dimensionalitymentioning

confidence: 99%

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

et al. 2022

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Machine learning algorithms have been used for the detection (and possibly) prediction of Alzheimer's disease using genotype information, with the potential to enhance the outcome prediction. However, detailed research about the analysis and the detection of Alzheimer's disease using genetic data is still in its primitive stage. The aim of this paper is to examine the scientific literature on the use of various machine learning approaches for the prediction of Alzheimer's disease based solely on genetic data. To identify gaps in the literature, critically appraise the reporting and methods of the algorithms, and provide the foundation for a wider research programme focused on developing novel machine learning based predictive algorithms in Alzheimer's disease. In our study between January 1, 2010, until September 21, 2021, we have reviewed different articles within PubMed, Web of Science, and Scopus to research into keywords and phrases linked to Alzheimer's disease and machine learning tools, including Artificial Neural Networks, boosting, and random forests. Articles were reviewed for inclusion, then retrieved, and assessed for risk of bias using Preferred Reporting Items for Systematic Reviews and Meta-analyses criteria. A pool of 150 abstracts, 65 full texts was evaluated and 24 studies were considered in the review. Machine learning methods in the reviewed papers performed in a wide range of ways (0.59 to 0.98 AUC). Our study indicated that high risk of bias in the analysis can be linked to feature selection, hyperparameter search and validation methods.

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Section: A the Curse Of Dimensionalitymentioning

confidence: 99%

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

et al. 2022

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“…With this approach, the AL aims to learn as much information as possible by querying as less unlabelled data as possible, particularly in the presence of sparse data. There are many other approaches developed in the SL (or ML) literature that particularly address the data sparsity phenomenon (Aremu, Hyland-Wood, & McAree, 2020;Brajard, Carrassi, Bocquet, & Bertino, 2020;Liu & Bellet, 2019;Nasiri, Minaei, & Sharifi, 2017;Suresh, Saraswathi, & Sundararajan, 2010;Wu, Zhang, Liu, Cai, & Cai, 2018).…”

Section: Mcda and Statistical Learningmentioning

confidence: 99%

Preference modelling in sorting problems: Multiple criteria decision aid and statistical learning perspectives

Erişkin

2021

Multi Criteria Decision Anal

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Many decision problems in a variety of fields such as marketing, quality prediction, and economics correspond to the sorting decision problematic where an ordinal scale is used to express a preference of objects. Both Multiple Criteria Decision Aid and Statistical Learning fields offer methodologies to represent the preference of the decision maker facing the sorting problem, however, there are differences in terminology, objectives, key assumptions, and solution philosophies. In this context, this paper aims to explain these differences as well as similarities and connections between these two fields by reviewing exemplary methodologies in sorting problems.As we discuss, there are significant research opportunities for developing new methodologies by exploiting the strong aspects of these two fields.

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“…Many algorithms, techniques, models, and tools are proposed to deal with this data. Feature selection research challenges related to feature selection are summarized in Table 1 [33][34][35].…”

Section: Related Workmentioning

confidence: 99%

Toward feature selection in big data preprocessing based on hybrid cloud-based model

2021

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Recently, big data are widely noticed in many fields like machine learning, pattern recognition, medical, financial, and transportation fields. Data analysis is crucial to converting data into more specific information fed to the decision-making systems. With the diverse and complex types of datasets, knowledge discovery becomes more difficult. One solution is to use feature subset selection preprocessing that reduces this complexity, so the computation and analysis become convenient. Preprocessing produces a reliable and suitable source for any data-mining algorithm. The effective features' selection can improve a model's performance and help us understand the characteristics and underlying structure of complex data. This study introduces a novel hybrid feature selection cloud-based model for imbalanced data based on the k nearest neighbor algorithm. The proposed model showed good performance compared with the simple weighted nearest neighbor. The proposed model combines the firefly distance metric and the Euclidean distance used in the k nearest neighbor. The experimental results showed good insights in both time usage and feature weights compared with the weighted nearest neighbor. It also showed improvement in the classification accuracy by 12% compared with the weighted nearest neighbor algorithm. And using the cloud-distributed model reduced the processing time up to 30%, which is deliberated to be substantial compared with the recent state-of-the-art methods.

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A machine learning approach to circumventing the curse of dimensionality in discontinuous time series machine data

Cited by 52 publications

References 34 publications

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review

Preference modelling in sorting problems: Multiple criteria decision aid and statistical learning perspectives

Toward feature selection in big data preprocessing based on hybrid cloud-based model

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