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Enhancing instance-based classification with local density: a new algorithm for classifying unbalanced biomedical data
2006
Bioinformatics
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Results: We present a novel instance-based classification technique which takes both information of different local density of data objects and local cluster structures into account. ...
In particular, classification on biomedical data often claims the separation of pathological and healthy samples with highest discriminatory performance for diagnostic issues. ...
ACKNOWLEDGEMENTS The authors thank Biocrates Life Sciences GmbH, Innsbruck, Austria for providing anonymized metabolic data. ...
doi:10.1093/bioinformatics/btl027
pmid:16443633
fatcat:36dnancodbex3owusigdo6axla
Cluster Density Properties Define a Graph for Effective Pattern Feature Selection
2020
IEEE Access
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feature selection algorithms. ...
Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. ...
, unbalanced class data amounts and densities, and noisy data. ...
doi:10.1109/access.2020.2981265
fatcat:vmgfcfexxbavjny3ocsrbpzvrm
Medical Health Big Data Classification Based on KNN Classification Algorithm
2019
IEEE Access
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Medical health big data provides a basic data resource guarantee for medical service intelligence and smart healthcare. ...
Aiming at the shortcomings of traditional KNN algorithm in processing large data sets, this paper proposes an improved KNN algorithm based on cluster denoising and density cropping. ...
In the new proposed design, this paper introduces a coefficient based on the unbalanced nature of the data set. ...
doi:10.1109/access.2019.2955754
fatcat:tfwq75ibtfgadblj3hs2mwiqoy
Adaptive Condensed Nearest Neighbor for Imbalance Data Classification
2019
International Journal of Intelligent Engineering and Systems
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Classification is a supervised learning method which acquires a training dataset to form its model for classifying unseen examples. ...
However, in unstructured data classification, the class boundary learned by standard machine learning algorithms can be severely skewed toward the target class. ...
The selection of Ada-CNN data will reduce the number of comparisons, which will automatically classify a new observation with a slight reduction in accuracy. ...
doi:10.22266/ijies2019.0430.11
fatcat:robbf4cwhrbwpblgg2mbqthmem
Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods
2020
IEEE Access
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To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required. ...
In this work, we review the more promising classification and optimization algorithms and reformulate them into a synergic framework to automatically design and optimize pattern classifiers. ...
Arturo González-Vega for his valuable comments about experimental results. ...
doi:10.1109/access.2020.2998749
fatcat:qufxajj66nampin3anpzfeqbhq
Involvement of Machine Learning Tools in Healthcare Decision Making
2021
Journal of Healthcare Engineering
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Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. ...
In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden ...
for density-based clustering algorithms [15] . ...
doi:10.1155/2021/6679512
pmid:33575021
pmcid:PMC7857908
fatcat:tkjpjybmife4vhugy4gq3f2tiy
Ensemble-based hybrid probabilistic sampling for imbalanced data learning in lung nodule CAD
2014
Computerized Medical Imaging and Graphics
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Classification plays a critical role in false positive reduction (FPR) in lung nodule computer aided detection (CAD). ...
To solve these challenges, we proposed a hybrid probabilistic sampling combined with diverse random subspace ensemble. ...
Scholarship Council for two years at the University of Alberta. ...
doi:10.1016/j.compmedimag.2013.12.003
pmid:24418073
fatcat:r6bw75plkzdhvb2v6sikowrn2m
Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future
2020
Applied Sciences
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Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. ...
The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous ...
By discussing advances in learning algorithms and classifiers, a new generic approach for multi-dimensional image analysis is proposed. ...
doi:10.3390/app10207201
fatcat:tofpvyllzbautos4my26xajqfe
Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions
2014
Biomedical Engineering
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The entropies and uniformities of the first two IMFs are then computed to form a feature vector that is fed to a support vector machine (SVM) for classification. ...
outperforms previous works found in the literature, with perfect classification. ...
[22] presented an automated system based on color normalization and local contrast enhancement, followed by fuzzy C-means clustering for image segmentation, and neural network (NN) classification. ...
doi:10.1515/bmt-2013-0082
pmid:24615482
fatcat:eltizdcalrfl3k4dgxbaazklfa
A Comparative Study on Bioinformatics Feature Selection and Classification
2012
International Journal of Computer Applications
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Established feature selection techniques based on principal component analysis (PCA), independent component analysis (ICA), genetic algorithm (GA) and support vector machine (SVM) are, for the first time ...
, applied to this data set to support learning and classification. ...
The KNN classifier works based on the intuition that the classification of an instance is likely to be most similar to the classification of other instances that are nearby to it within the vector space ...
doi:10.5120/6081-8219
fatcat:ky26i3xcwra73cvl7qj47rx6um
Development of heart attack prediction model based on ensemble learning
2021
Eastern-European Journal of Enterprise Technologies
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With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic ...
The proposed system involves preprocessing data, selecting attributes, and then using logistic regression algorithms as meta-classifiers to build the ensemble learning model. ...
on the dataset; -level one data: it takes the prediction produced by the classification algorithms as new data; -final prediction: it is another new learning process, it takes the level one data as new ...
doi:10.15587/1729-4061.2021.238528
fatcat:vd2buj5bzbcsllbbyofbynretm
A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project
[article]
2021
medRxiv
pre-print
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The healthy aging scale has been constructed based on a selection of particular variables from 16 individual studies. ...
We show that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering within a clustering for ensemble classification scheme while retaining prediction benefits. ...
The large data volume on each biomedical research field offers the opportunity to open new avenues for exploring the various biomedical phenomena. ...
doi:10.1101/2021.01.23.21250355
fatcat:wybczqevgvg3bkvh4pwv36znxe
Pattern Recognition Software and Techniques for Biological Image Analysis
2010
PLoS Computational Biology
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Here, we provide a brief overview of the technologies behind pattern recognition and its use in computer vision for biological and biomedical imaging. ...
This approach relies on training a computer to recognize patterns in images rather than developing algorithms or tuning parameters for specific image processing tasks. ...
For instance, if a classifier of two classes has 40 test images of class A and 10 test images of class B, correct classification of all class A images will lead to an accuracy of 80%, even if the classifier ...
doi:10.1371/journal.pcbi.1000974
pmid:21124870
pmcid:PMC2991255
fatcat:a63kdds4grcoxiy3yyp25526hm
Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem
[chapter]
2002
Lecture Notes in Computer Science
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We propose combining advanced statistical approaches with data mining techniques to build classifiers to enhance decision-making models for the job assignment problem. ...
We apply our techniques to the problem of assigning jobs to Navy officers, with the goal of enhancing happiness for both the Navy and the officers. ...
These include fewer assumptions, less complexity of the model and algorithms, less likely to be trapped at local minimum, can deal with data with high noise level and data with large number of attributes ...
doi:10.1007/3-540-47887-6_43
fatcat:7h6t526eprcl7o33s7urbbow5m
A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets
2024
Heliyon
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This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. ...
The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical ...
For instance Ref. [143] , proposed a hybrid model that integrates clinical data, neuroimaging data, and deep learning algorithms to diagnose Parkinson's disease with high accuracy. ...
doi:10.1016/j.heliyon.2024.e25469
pmid:38356538
pmcid:PMC10865258
fatcat:57irrufsrrbr5bumu3cfmsazq4
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