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Wrapper Methods to Correct Mislabelled Training Data
2013 International Workshop on Pattern Recognition in Neuroimaging, 2013Machine learning has obvious applications to the diagnosis of disease, and for many neurological conditions features extracted from brain images allow classifiers based on neuroimaging biomarkers to provide a useful complement to more traditional diagnostic methods based on symptoms and psychological testing.
Jonathan Young +2 more
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A novel wrapper method for feature selection and its applications
Neurocomputing, 2015This paper introduces a wrapper method, namely cosine similarity measure support vector machines (CSMSVM), to eliminate irrelevant or redundant features during classifier construction by introducing the cosine distance into support vector machines (SVM).
Gang Chen 0024, Jin Chen
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Wrapper method for feature selection to classify cardiac arrhythmia
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017Efficient monitoring of cardiac patients can save tremendous amount of lives. Cardiac disease prediction and classification has gained utmost significance in this regard during the past few years. This paper presents a predictive model for classification of arrhythmias.
Anam Mustaqeem +3 more
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HVS-MRMR wrapper method for variables selection
2017 Intelligent Systems and Computer Vision (ISCV), 2017This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). Our algorithm based on wrapper approach using multi-layer perceptron. We call this algorithm a HVS-MRMR Wrapper for variables selection.
Ben-Hdech Adil +2 more
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A genetic enhancement to the wrapper method
IEE Colloquium on Knowledge Discovery and Data Mining, 1996In the wrapper approach to feature subset selection the learning algorithm itself is used in the selection process, thus making the process sensitive to the bias of the learning algorithm. The approach introduces a useful framework within which search algorithms and classifiers can be compared.
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A hybrid feature selection approach based on statistical and wrapper methods
2016 24th Signal Processing and Communication Application Conference (SIU), 2016Gene expression datasets contain a lot of gene expressions. It is very important to identify only relevant genes on huge datasets. Thus, feature selection is very important process. In this study, it is suggested a method which obtains both fast and high classification accuracy.
Mahmut Kaya, Hasan Sakir Bilge
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A Wrapper Method for Feature Selection in Multiple Classes Datasets
2009Feature selection algorithms should remove irrelevant and redundant features while maintaining or even improving performance, and thus contributing to enhance generalization in learning models. Feature selection methods can be mainly grouped into filters and wrappers.
Noelia Sánchez-Maroño +2 more
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Filter/Wrapper Methods for Gene Selection and Classification of Microarray Dataset
International Journal of Software Innovation, 2019A wide variety of large-scale information has been made within the extraction of genomic information and the extraction of data. Problems addressed embody ordination sequencing, supermolecule structure modeling, or the reconstruction of biological process trees (phylogeny). These issues need collaboration between biologists and computer scientists as a
Norreddine Mekour +2 more
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Feature Selection For The Prediction Of Tropospheric Ozone Concentration Using A Wrapper Method
Intelligent Automation & Soft Computing, 2011High concentrations of ozone (03) in the lower troposphere increase global warming, and thus affect climatic conditions and human health.
Cemal Okan Sakar +5 more
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An Efficient Wrapper Scan Chain Configuration Method for Network-on-Chip Testing
IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures (ISVLSI'06), 2006Network-on-chip (NoC) is the new paradigm for core-based system design. Reuse of the on-chip communication network for testing cores in a NoC-based system is critical to reduce test cost for this new architecture. However, many new challenging issues come up correspondingly. In this paper, we propose a test data transportation method with multiple data
Ming Li, Wen-Ben Jone, Qing-An Zeng
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