Multiple Classification Using Logistic Regression Model

Zou, Baoping

doi:10.1007/978-3-319-51969-2_21

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…LR is a generalized linear regression analysis model that can handle both classification and regression problems. The improved LR can even handle multiclassification problems [ 39 ]. The relationship between the intermediate value y and the input x represents the linear part of the model, which is y = ∑ i =1 m ( w i ∗ x i )+ b , where w i is the weight matrix and b is the bias.…”

Section: Methodsmentioning

confidence: 99%

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Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning

Gui

Chen

Nie

2020

Computational Intelligence and Neuroscience

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In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert–Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.

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Section: Methodsmentioning

confidence: 99%

“…generalized linear regression analysis model that can handle both classification and regression problems. e improved LR can even handle multiclassification problems [39]. e relationship between the intermediate value y and the input…”

Section: Logistic Regression For Multiclassification Lr Is Amentioning

confidence: 99%

Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning

Gui

Chen

Nie

2020

Computational Intelligence and Neuroscience

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“…2) Model building and error analysis: Based on the data in Sheet 2 of the attachment, we divide it into four subcategories of data, that is, four data sets, and perform k-means clustering analysis, using the mean square of chemical composition as the criterion for selecting subcategory division; then we use the logistic model to test the effect of subcategory division [10].…”

Section: K-means Clusteringmentioning

confidence: 99%

Research on classification of weathered glass based on logistic regression

Zhang,

Li,

Zheng

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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In order to classify glass artifacts into different categories based on two characteristics: the chemical composition of the sampling points and whether they are weathered or not, this paper is divided into two parts. In the first part, the entire dataset is divided using data preprocessing, and a logistic regression model is constructed for binary classification. The optimal parameters of the model are estimated using maximum likelihood estimation and gradient descent algorithm. The aim is to explore the classification patterns of the two types of glass artifacts. In the second part, unsupervised learning kmeans algorithm is used for clustering. Indicators are selected based on mean square error and confidence level. The same model as in the first part is used to test the effectiveness of sub-classification. The results show that glass artifacts can be divided into two main categories: high-potassium glass and lead-barium glass, and further subdivided into 14 subcategories.

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“…Since the dimension of the original data has no effect on the size of the kernel matrix, the kernel function method can effectively deal with the high-dimensional data, thus avoiding the dimension disaster problem of the traditional pattern recognition methods. In addition, logistic regression (LR) [47][48][49] uses the regression function to classify the features into one or multiple classes. These conventional methods can achieve classification tasks easily, but they consider the unlabelled samples independently and neglect the inter-class and intra-class property, which leads to these methods failing to gain robust classification results.…”

Section: Related Workmentioning

confidence: 99%

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

2018

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When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

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Multiple Classification Using Logistic Regression Model

Cited by 3 publications

References 11 publications

Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning

Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning

Research on classification of weathered glass based on logistic regression

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

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