With the continuous development of information technology and computer industry, data processing has become a top priority. We want to do a good job of data processing, it is necessary to apply to the data classificat...
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With the continuous development of information technology and computer industry, data processing has become a top priority. We want to do a good job of data processing, it is necessary to apply to the data classification algorithm, which as a key technology in data mining can be a good job to complete the data processing. In this paper, by comparing several different data classification algorithms, to find their similarities and differences to further promote the data classification algorithm to lay the foundation
K nearest neighbor（KNN） algorithm has been widely used as a simple and effective classification algorithm. The traditional KNN classification algorithm will find k nearest neighbors, it is necessary to calculate the...
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ISBN:
(纸本)9781510842915
K nearest neighbor（KNN） algorithm has been widely used as a simple and effective classification algorithm. The traditional KNN classification algorithm will find k nearest neighbors, it is necessary to calculate the distance from the test sample to all training samples. When the training sample data is very large, it will produce a high computational overhead, resulting in a decline in classification speed. Therefore, we optimize the distance calculation of the KNN algorithm. Since KNN only considers the k samples of the shortest distance from the test sample to the nearest training sample point, the large distance training has no effect on the classification of the algorithm. The improved method is to sample the training data around the test data, which reduces the number of distance calculation of the test data to each training data, and reduces the time complexity of the algorithm. The experimental results show that the optimized KNN classification algorithm is superior to the traditional KNN algorithm.
With the development of Internet finance, in the field of financial anti-fraud, more and more accurate methods are needed to make users and enterprises have a two-way credit guarantee. This paper mainly studies the cl...
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With the development of Internet finance, in the field of financial anti-fraud, more and more accurate methods are needed to make users and enterprises have a two-way credit guarantee. This paper mainly studies the classification algorithm of machine learning, especially the stochastic forest algorithm. And applies it to the field of financial anti-fraud, and determines whether the user's credit in the overdue judgment of the user's loan.
Based on the traditional fuzzy BP classification method,the image with high degree of feature phase was classified with higher misclassification *** the problems of the traditional methods,in this paper,a classificati...
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Based on the traditional fuzzy BP classification method,the image with high degree of feature phase was classified with higher misclassification *** the problems of the traditional methods,in this paper,a classification feature similarity image classification algorithm based on deep learning and support vector machine was ***,the local average noise reduction method was used to denoise the similarity image,and the wavelet image was decomposed by wavelet multi-scale decomposition ***,the local information smoothing processing of the image was performed by the RGB color component recombination method,and the rough set feature quantity of the image was ***,the extracted feature quantities were input into a support vector machine learner for image *** the hidden layer of the classifier,adaptive learning of weighting parameters was performed by the deep learning algorithm to achieve image enhancement processing and classification optimization of batch feature similarity *** simulation results showed that the accuracy of feature similarity image classification was better,the ability to resist inter-class attribute perturbation was stronger,and the retrieval efficiency of large-scale images was improved.
With the developing of big data application,classification algorithm has been expanded to distributed datasets from the single *** a dynamic integrated classification algorithm based on big data environment was *** al...
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With the developing of big data application,classification algorithm has been expanded to distributed datasets from the single *** a dynamic integrated classification algorithm based on big data environment was *** algorithm gain integrated classifiers of high classification accuracy for each local dataset,and dynamically generate the recognition model according to the distribution characteristics of local samples to be *** the application process,after numerous new sample data join the datasets,the classifier performance will drop *** aiming at the above problem,this algorithm will retrain the classification model in the dynamic expansion process of *** to the experimental results,the algorithm proposed in this paper has high classifier training performance and classification *** the same time,it also possesses high adaptive capacity when faced with dynamically changing distributed datasets.
Aiming at the problem that the traditional classification algorithm has low classification accuracy,a KNN classification algorithm based on STORM big data is *** giving the basic information of the classification algo...
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Aiming at the problem that the traditional classification algorithm has low classification accuracy,a KNN classification algorithm based on STORM big data is *** giving the basic information of the classification algorithm,extract the data features,propose a description of the improved KNN classification method,introduce STORM big data,and optimize the KNN classification *** experimental results show that the improved algorithm has higher classification accuracy and has certain advantages.
Ultrafast, high-intensity X-ray free-electron lasers can perform diffraction imaging of single protein molecules. Various algorithms have been developed to determine the orientation of each single-particle diffraction...
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Ultrafast, high-intensity X-ray free-electron lasers can perform diffraction imaging of single protein molecules. Various algorithms have been developed to determine the orientation of each single-particle diffraction pattern and reconstruct the 3D diffraction intensity. Most of these algorithms rely on the premise that all diffraction patterns originate from identical protein molecules. However, in actual experiments, diffraction patterns from multiple different molecules may be collected simultaneously. Here, we propose a predicted model-aided one-step classification-multireconstruction algorithm that can handle mixed diffraction patterns from various molecules. The algorithm uses predicted structures of different protein molecules as templates to classify diffraction patterns based on correlation coefficients and determines orientations using a correlation maximization method. Tests on simulated data demonstrated high accuracy and efficiency in classification and reconstruction.
This paper proposes a weighted C-SVM algorithm and analyzes its classification performance theoretically. This weighted C-SVM introduces weight factors for classes and samples. Experiments show that C-SVM can effectiv...
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This paper proposes a weighted C-SVM algorithm and analyzes its classification performance theoretically. This weighted C-SVM introduces weight factors for classes and samples. Experiments show that C-SVM can effectively solve the misclassification problem resulted from the imbalance in the number of training samples of different classes and the problem that important samples are misclassified.
We introduce the multi-width of a lattice polytope and use this to classify and count all lattice tetrahedra with multi-width ( 1 , w 2 , w 3 ) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \...
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We introduce the multi-width of a lattice polytope and use this to classify and count all lattice tetrahedra with multi-width ( 1 , w 2 , w 3 ) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(1,w_2,w_3)$$\end{document} . The approach used in this classification can be extended into a computer algorithm to classify lattice tetrahedra of any given multi-width. We use this to classify tetrahedra with multi-width ( 2 , w 2 , w 3 ) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(2,w_2,w_3)$$\end{document} for small w 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w_2$$\end{document} and w 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w_3$$\end{document} and make conjectures about the function counting lattice tetrahedra of any multi-width.
Software Defect Prediction (SDP) empowers the creators to diagnose and unscramble defects in the introductory legs of the software evolution process to reduce the effort and cost invested in creating high-quality soft...
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Software Defect Prediction (SDP) empowers the creators to diagnose and unscramble defects in the introductory legs of the software evolution process to reduce the effort and cost invested in creating high-quality software. Feature Selection (FS) is critical to pinpoint the most pertinent features for defect prediction. This paper intends to employ a peculiar wrapper-based FS mode, dubbed DAOAFS, rooted on the dynamic arithmetic optimization algorithm (DAOA). Subsequently, this work evaluates the competence of the proposed FS mode using ten benchmark NASA datasets on four supervised learning classifiers, namely NB, DT, SVM, and KNN using accuracy and error curve as the standard performance measure metrics. This paper also correlates the proposed FS mode's conduct with existing FS techniques based on widely utilized meta-heuristic approaches such as GA, PSO, DE, ACO, FA, and SWO. This work employed Friedman and Holm test to ratify the proposed FS mode's statistical connotation. The investigatory outcomes supported the assertion that the recommended DAOAFS mode was effective in enhancing the efficacy of the defect forecasting model by achieving the highest mean accuracy of 94.76%. The findings also revealed that the proposed approach established its supremacy over the other studied FS techniques with bettered veracity in most instances.
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