AIMC Topic: Cluster Analysis

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Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis.

Computer assisted surgery (Abingdon, England) Aug 12, 2019
To overcome the two-class imbalanced classification problem existing in the diagnosis of breast cancer, a hybrid of Random Over Sampling Example, K-means and Support vector machine (RK-SVM) model is proposed which is based on sample selection. Random...
Support Vector Machine Cluster Analysis Data Mining Humans Female Breast Neoplasms Datasets as Topic
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Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 9, 2019
Neuroprosthesis enables the brain control on the external devices purely using neural activity for paralyzed people. Supervised learning decoders recalibrate or re-fit the discrepancy between the desired target and decoder's output, where the correct...
Machine Learning Algorithms Haplorhini Movement Reinforcement, Psychology Motor Cortex Electrodes, Implanted Neural Prostheses Cluster Analysis Attention Brain-Computer Interfaces Computer Simulation Animals
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Adaptive robust principal component analysis.

Neural networks : the official journal of the International Neural Network Society Aug 1, 2019
Robust Principal Component Analysis (RPCA) is a powerful tool in machine learning and data mining problems. However, in many real-world applications, RPCA is unable to well encode the intrinsic geometric structure of data, thereby failing to obtain t...
Cluster Analysis Principal Component Analysis Data Mining Machine Learning Algorithms
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The GH-EXIN neural network for hierarchical clustering.

Neural networks : the official journal of the International Neural Network Society Jul 31, 2019
Hierarchical clustering is an important tool for extracting information from data in a multi-resolution way. It is more meaningful if driven by data, as in the case of divisive algorithms, which split data until no more division is allowed. However, ...
Humans Cluster Analysis Information Storage and Retrieval Algorithms Neural Networks, Computer Colorectal Neoplasms Gene Expression Regulation, Neoplastic
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Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks.

International journal of computer assisted radiology and surgery Jul 30, 2019
PURPOSE: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unpreceden...
Motion Humans Markov Chains Neural Networks, Computer General Surgery Machine Learning Regression Analysis Robotic Surgical Procedures Clinical Competence Feedback Cluster Analysis Biomechanical Phenomena Models, Statistical Surgeons
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Consistency and differences between centrality measures across distinct classes of networks.

PloS one Jul 26, 2019
The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measur...
Multivariate Analysis Models, Theoretical Humans Cluster Analysis Neural Networks, Computer Principal Component Analysis
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On how to not misuse hierarchical clustering on principal components to define clinically meaningful patient subgroups. Response to: 'On using machine learning algorithms to define clinical meaningful patient subgroups' by Pinal-Fernandez and Mammen.

Annals of the rheumatic diseases Jul 24, 2019
Humans Machine Learning Algorithms Cluster Analysis
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Mapping differential responses to cognitive training using machine learning.

Developmental science Jul 22, 2019
We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network...
Child Cognition Cluster Analysis Adolescent Child, Preschool Cognition Disorders Unsupervised Machine Learning Intelligence Memory, Short-Term Humans Female Male
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Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools.

Nutrients Jul 22, 2019
Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macr...
Humans Neural Networks, Computer Nutrients Nutritional Status Postmenopause Cluster Analysis Machine Learning Models, Biological Female Body Weight Exercise
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Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data.

Journal of proteome research Jul 17, 2019
Deep learning (DL), a type of machine learning approach, is a powerful tool for analyzing large sets of data that are derived from biomedical sciences. However, it remains unknown whether DL is suitable for identifying contributing factors, such as b...
Cluster Analysis Pancreatic Neoplasms Bayes Theorem Proteomics Support Vector Machine Mass Spectrometry Humans Deep Learning Machine Learning Logistic Models Algorithms
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