ArticleComputational and mathematical methods in medicine2015
Binary matrix shuffling filter for feature selection in neuronal morphology classification.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Research on the Application of Multi-Source Information Fusion in Multiple Gait Pattern Transition Recognition.Sensors (Basel, Switzerland) · 2022Article
- Mining Big Neuron Morphological Data.Computational intelligence and neuroscience · 2018Review
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5 authors.
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Abstract
A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained.
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