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- W2998889333 abstract "The neuron classification problem is significant for understanding structure-function relationships in computational neuroscience. By solving neuronal classification problem, we can further understand the characteristic of neurons and the process of information transmission. In the training process of the neuron geometry classifier, selecting the appropriate neuron feature data is the premise of obtaining good classification results. First, a data set of 200 three-dimensional neurons was constructed, and there were 10 different types of neurons. Secondly, according to the difference of the extracted neuron characteristic parameters, it is divided into two categories: global features and distribution features. Finally, the machine learning methods such as Logistic Regression, K-Nearest Neighbor, Naive Bayes, Decision Tree, Random Forest, Extremely Randomized Trees and Extreme Gradient Boosting are used to classify neurons, and the effects of different features on neuron classification results are analyzed. The experimental results show: (1) For the K-Nearest Neighbor and Naive Bayes, the distribution feature of the neuron is more critical than the global feature, and even if the effect of the combination of the two will not be improved; (2) The ensemble classifier based on decision trees, such as Random Forests, Extremely Randomized Trees and Extreme Gradient Boosting have better accuracy and stability for neuron classification." @default.
- W2998889333 created "2020-01-23" @default.
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- W2998889333 date "2019-08-01" @default.
- W2998889333 modified "2023-09-23" @default.
- W2998889333 title "Quantitative Analysis of Influence of Morphological Feature Selection on Neuron Classification" @default.
- W2998889333 doi "https://doi.org/10.1109/ihmsc.2019.10145" @default.
- W2998889333 hasPublicationYear "2019" @default.
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