Data analysis for a set of university student lists using the k-Nearest Neighbors machine learning method

<span>The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors.</span>

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A unified machine learning method for task-related and resting state fMRI data analysis

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2014.6945099 ◽

2014 ◽

Author(s):

Xiaomu Song ◽

Nan-kuei Chen

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Machine Learning Method ◽

Learning Method ◽

Fmri Data Analysis

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Studying the capabilities of the analytical system based on the machine learning method

Radio Industry (Russia) ◽

10.21778/2413-9599-2020-30-3-112-126 ◽

2020 ◽

Vol 30 (3) ◽

pp. 112-126

Author(s):

S. V. Palmov

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Predictive Models ◽

Arithmetic Mean ◽

Machine Learning Method ◽

Learning Method ◽

Learning Tools ◽

Analytical System ◽

Reliability And Robustness ◽

Almost All

Data analysis carried out by machine learning tools has covered almost all areas of human activity. This is due to a large amount of data that needs to be processed in order, for example, to predict the occurrence of specific events (an emergency, a customer contacting the organization’s technical support, a natural disaster, etc.) or to formulate recommendations regarding interaction with a certain group of people (personalized offers for the customer, a person’s reaction to advertising, etc.). The paper deals with the possibilities of the Multitool analytical system, created based on the machine learning method «decision tree», in terms of building predictive models that are suitable for solving data analysis problems in practical use. For this purpose, a series of ten experiments was conducted, in which the results generated by the system were evaluated in terms of their reliability and robustness using five criteria: arithmetic mean, standard deviation, variance, probability, and F-measure. As a result, it was found that Multitool, despite its limited functionality, allows creating predictive models of sufficient quality and suitable for practical use.

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Incomplete Data Analysis

Applications of Pattern Recognition ◽

10.5772/intechopen.94068 ◽

2021 ◽

Author(s):

Bo-Wei Chen ◽

Jia-Ching Wang

Keyword(s):

Machine Learning ◽

Pattern Recognition ◽

Data Analysis ◽

Future Development ◽

Missing Values ◽

Regression Tree ◽

Nearest Neighbors ◽

K Nearest Neighbors ◽

Numerical Examples ◽

Single Imputation

This chapter discusses missing-value problems from the perspective of machine learning. Missing values frequently occur during data acquisition. When a dataset contains missing values, nonvectorial data are generated. This subsequently causes a serious problem in pattern recognition models because nonvectorial data need further data wrangling before models are built. In view of such, this chapter reviews the methodologies of related works and examines their empirical effectiveness. At present, a great deal of effort has been devoted in this field, and those works can be roughly divided into two types — Multiple imputation and single imputation, where the latter can be further classified into subcategories. They include deletion, fixed-value replacement, K-Nearest Neighbors, regression, tree-based algorithms, and latent component-based approaches. In this chapter, those approaches are introduced and commented. Finally, numerical examples are provided along with recommendations on future development.

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Explainable t-SNE for single-cell RNA-seq data analysis

10.1101/2022.01.12.476084 ◽

2022 ◽

Author(s):

Henry Han ◽

Tianyu Zhang ◽

Mary Lauren Benton ◽

Chun Li ◽

Juan Wang ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Data Analysis ◽

Dimension Reduction ◽

Single Cell ◽

Method Development ◽

Robustness Analysis ◽

High Dimensional ◽

Machine Learning Method ◽

Learning Method

Single-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve the corresponding low-dimensional embeddings of input scRNA-seq data to conduct clustering. However, the methods lack explainability and may not perform well with scRNA-seq data because they are often migrated from other fields and not customized for high-dimensional sparse scRNA-seq data. In this study, we propose an explainable t-SNE: cell-driven t-SNE (c-TSNE) that fuses the cell differences reflected from biologically meaningful distance metrics for input scRNA-seq data. Our study shows that the proposed method not only enhances the interpretation of the original t-SNE visualization for scRNA-seq data but also demonstrates favorable single cell segregation performance on benchmark datasets compared to the state-of-the-art peers. The robustness analysis shows that the proposed cell-driven t-SNE demonstrates robustness to dropout and noise in dimension reduction and clustering. It provides a novel and practical way to investigate the interpretability of t-SNE in scRNA-seq data analysis. Unlike the general assumption that the explainanbility of a machine learning method needs to compromise with the learning efficiency, the proposed explainable t-SNE improves both clustering efficiency and explainanbility in scRNA-seq analysis. More importantly, our work suggests that widely used t-SNE can be easily misused in the existing scRNA-seq analysis, because its default Euclidean distance can bring biases or meaningless results in cell difference evaluation for high-dimensional sparse scRNA-seq data. To the best of our knowledge, it is the first explainable t-SNE proposed in scRNA-seq analysis and will inspire other explainable machine learning method development in the field.

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