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International Symposium on Computer, Consumer and Control (IS3C)

作者： Yang, Jie Gao, Hong Liu, Wei Dalian Maritime Univ Transportat Management Coll 1Linghai Rd Dalian 116026 Peoples R China Dalian Maritime Univ Dept Math 1Linghai Rd Dalian 116026 Peoples R China

ISBN: (纸本)9781509030712

The purpose of this study is to propose an approach to recommend classification algorithms for real-world classification problems. First, the extension rhombus thinking mode is used to construct performance indicators of the classification algorithm. Second, an extension-based recommendation method about classification algorithm is proposed. Third, a recommendation system is designed to implement our recommendation method. The system input is divided into two parts: expert knowledge and user preference. In order to reduce user workloads and confusions about parameter settings, a real-time interaction progressive strategy is proposed to help users complete their inputs. Additionally, the case function is designed so that users can get more support information from current and previous cases. At last, an application is used to verify the effectiveness of our system. And the application result shows that our system can provide an efficient and intelligent decision support for users.

关键词： classification algorithm Recommendation system Decision support Extension rhombus thinking mode Extension-based innovation method

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Improved classification algorithm Based on Genetic Programming and Its Application in Process Monitoring of Additive Manufacturing 24

Improved Classification Algorithm Based on Genetic Programmi...

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24th ISPE Inc International Conference on Transdisciplinary Engineering (TE) Focusing on Collaborative Systems in Transdisciplinary Engineering

作者： Yang, Zhensheng Huang, Youfang Shanghai Maritime Univ Coll Logist Engn Shanghai 201306 Peoples R China Shanghai Maritime Univ Minist Educ Engn Res Ctr Container Supply Chain Technol Shanghai 201306 Peoples R China

ISBN: (纸本)9781614997795;9781614997788

This work presented a defect classification methods based on improved classification algorithm in additive manufacturing process. To make the algorithm be applicable in process monitoring tasks, a method of optimizing the evolution process in GP evolution was raised in this work. A series of specific functions and their linear combinations were introduced to represent the GP classification model. The evolution process in this strategy is designed to optimize the coefficients of these functions and the offset. The advantaged in GP are also completely inherited. Comparing with GP alone, the improved strategy could reach higher classification accuracy in engineering application, i.e., process monitoring of additive manufacture.

关键词： classification algorithm Genetic Programming Additive manufacture Process monitoring

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classification algorithm based on multiple hyper-spheres classifier and KNN method

Classification algorithm based on multiple hyper-spheres cla...

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2014 International Conference on Industrial Electronics and Engineering (ICIEE 2014)

作者： Gongjing Zhang Zhongshuai Zhao Fa Chen College of Information Engineering Qingdao University

This paper proposes a classification algorithm including two stages. In training stage, multiple hyper-spheres are generated respectively to enclose every category of samples following the order from the biggest hyper-sphere to the smallest one, and some of hyper-spheres are discarded while they only enclose one sample. In testing stage, testing samples are checked by using hyper-spheres firstly, and the category of a sample is recognized by the enclosing hyper-sphere. If a sample is not surrounded by any hyper-sphere, KNN method is used to decide its category. Four real datasets are utilized to do experiments. The results show better performance than two multiple hyper-spheres classification algorithms and attest the effectiveness of this algorithm.

关键词： priority of the biggest hyper-sphere multiple hyper-spheres classifier KNN method classification algorithm

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Application of ID3 Decision Tree classification algorithm in Mathematical data Analysis

Application of ID3 Decision Tree Classification Algorithm in...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Zang, Ping Shandong Transport Vocational College Shandong Weifang China

ISBN: (纸本)9798350315455

ID3 decision tree is the most extensive method. It can be used to classify, identify and predict data information, and the whole system can be divided into several stages according to its nature. It plays an important role in mathematical data analysis. Therefore, based on ID3 decision classification algorithm, this paper studies mathematical data analysis. This paper firstly introduces the decision tree classification algorithm, and paper expounds the ID3 decision tree classification algorithm in data analysis, the application of mathematics algorithm in the application of mathematical data analysis process is introduced, and the performance of ID3 decision tree classification algorithm was tested. Finally, the test results show that for a high-resolution decision tree, if the traditional method is used for classification, it will increase the amount of calculation and time. When the ID3 decision number algorithm is used to improve the model, it can effectively improve the decision accuracy. The mathematical data analysis time based on the ID3 decision number algorithm increases with the increase of the number of data. However, the overall time is within 1-4 seconds, and at the same time, the classification accuracy is more than 93%, which shows that the performance of the algorithm is good. © 2023 IEEE.

关键词： classification algorithm data analysis ID3 decision tree mathematical data model improvement

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Ensemble of ML-KNN for classification algorithm recommendation

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KNOWLEDGE-BASED SYSTEMS 2021年 221卷 106933-106933页

作者： Zhu, Xiaoyan Ying, Chenzhen Wang, Jiayin Li, Jiaxuan Lai, Xin Wang, Guangtao Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China JD AI Res Mountain View CA USA

With the mountains of classification algorithms proposed in the literature, the study of how to select suitable classifier(s) for a given problem is important and practical. Existing methods rely on a single learner built on one type of meta-features or a simple combination of several types of meta-features to address this problem. In this paper, we propose a two-layer classification algorithm recommendation method called EML (Ensemble of ML-KNN for classification algorithm recommendation) to leverage the diversity of different sets of meta-features. The proposed method can automatically recommend different numbers of appropriate algorithms for different dataset, rather than specifying a fixed number of appropriate algorithm(s) as done by the ML-KNN, SLP-based and OBOE methods. Experimental results on 183 public datasets show the effectiveness of the EML method compared to the three baseline methods. (c) 2021 Elsevier B.V. All rights reserved.

关键词： classification algorithm Recommendation method Ensemble learning

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Persistent homology classification algorithm

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1195页

作者： Lara, Mark LexterD. De Univ Philippines Los Banos Inst Math Sci & Phys Coll Arts & Sci Los Banos Laguna Philippines Univ Philippines Diliman Inst Math Quezon City Metro Manila Philippines

Data classification is an important aspect of machine learning, as it is utilized to solve issues in a wide variety of contexts. There are numerous classifiers, but there is no single best-performing classifier for all types of data, as the no free lunch theorem implies. Topological data analysis is an emerging topic concerned with the shape of data. One of the key tools in this field for analyzing the shape or topological properties of a dataset is persistent homology, an algebraic topology-based method for estimating the topological features of a space of points that persists across several resolutions. This study proposes a supervised learning classification algorithm that makes use of persistent homology between training data classes in the form of persistence diagrams to predict the output category of new observations. Validation of the developed algorithm was performed on real-world and synthetic datasets. The performance of the proposed classification algorithm on these datasets was compared to that of the most widely used classifiers. Validation runs demonstrated that the proposed persistent homology classification algorithm performed at par if not better than the majority of classifiers considered.

关键词： Persistent homology Supervised learning classification algorithm Topological data analysis

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A classification algorithm based on regions' luminance distribution applying to fractal image compression

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2005年第9期E88D卷 2223-2227页

作者： Zhou, CG Meng, K Qiu, ZL Xian Jiaotong Univ Dept Autocontrol Xian 710049 Peoples R China

This paper present three characteristic functions which can express the luminance distribute characteristic much better. Based on these functions a region classification algorithm is presented. The algorithm can offer more information on regions' similarity and greatly improve the efficiency and performance of match seeking in fractal coding. It can be widely applied to many kinds of fractal coding algorithms. Analysis and experimental results proved that it can offer more information on luminance distribute characteristics among regions and greatly improve the decoding quality and compression ratio with holding the running speed.

关键词： fractal coding image compression match seeking classification algorithm

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A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features

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ENERGY 2022年第PartA期244卷 122706-122706页

作者： He, Huizi Sun, Mei Li, Xiuming Mensah, Isaac Adjei Jiangsu Univ Ctr Energy Dev & Environm Protect Zhenjiang 212013 Jiangsu Peoples R China Changshu Inst Technol Sch Math & Stat Changshu 215500 Jiangsu Peoples R China

Reliable forecasting of crude oil price has received a prodigious attention by both investment companies and governments. Motivated by this issue, this paper seeks to propose a new hybrid forecasting model for crude oil price trend prediction. For this purpose, the crude oil price series is initially decomposed by variational mode decomposition algorithm, and the multi-modal data features are extracted based on the decomposed modes. The volatility of crude oil prices is simultaneously converted into trend symbols through symbolic time series analysis. Machine learning multi-classifier are then trained with multi -modal data features and historical volatility as input and trend symbols as output. The well-trained models are used to predict the trend symbols of West Texas Intermediate crude oil future price. Empirical results demonstrate that the proposed hybrid forecasting model outperforms its counterparts. Among the classifiers used, the hybrid prediction model using support vector machine classifier exhibits superior predictive ability. The accuracy of the proposed model for predicting high volatility of crude oil prices is evidenced to be better than that of low volatility. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Crude oil price prediction Multi -modal data features Machine learning classification algorithm Variational mode decomposition

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A classification algorithm for High-dimensional Data

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Procedia Computer Science 2015年 53卷 345-355页

作者： Asim Roy Department of Information Systems Arizona State University Tempe Arizona USA

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.

关键词： Kohonen nets classification algorithm online learning feature selection high-dimensional data

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A classification algorithm Based on Improved Locally Linear Embedding

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2024年第1期18卷 1页

作者： Wang, Hui Cai, Tie Cheng, Dongsheng Li, Kangshun Zhou, Ying Shenzhen Inst Informat Technol Shenzhen Peoples R China Dongguan City Univ Dongguan Peoples R China

The current classification is difficult to overcome the high-dimension classification problems. So, we will design the decreasing dimension method. Locally linear embedding is that the local optimum gradually approaches the global optimum, especially the complicated manifold learning problem used in big data dimensionality reduction needs to find an optimization method to adjust k-nearest neighbors and extract dimensionality. Therefore, we intend to use orthogonal mapping to find the optimization closest neighbors k, and the design is based on the Lebesgue measure constraint processing technology particle swarm locally linear embedding to improve the calculation accuracy of popular learning algorithms. So, we propose classification algorithm based on improved locally linear embedding. The experiment results show that the performance of proposed classification algorithm is best compared with the other algorithm.

关键词： classification algorithm Locally Linear Embedding Orthogonal Mapping

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