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An Efficient classification algorithm on Orchid Species Based on Data Enhancement

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2025年第1期21卷 52-62页

作者： Wang, Jianhua Wang, Haozhan South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou Peoples R China Natl Agr Aviat Pesticide Precis Spraying Technol R Guangzhou Peoples R China

In order to address the issue of low accuracy rate of current orchid type classification methods due to their similarities in the characteristics of orchid types, an effective orchid type classification method using data enhancement is suggested in this work, whose contribution depends on the utilization of data enhancement technologies, which can efficiently enhance the orchid type classification accuracy rate by providing sufficient and balanced sample sets. Specifically, in our approach, firstly, an image set of 12 orchid types containing 12,227 images is established;secondly, the characteristics of the above orchid image dataset are analyzed and studied;thirdly, the reasons for the processing difficulties are identified based on the above orchid image set;at last, some data enhancement technologies are applied to improve the classification accuracy rate of orchid types, which can also enhance the whole performance of orchid type classification. The experimental results display that our suggested classification method using data enhancement in the article can achieve a classification accuracy of 92.65% compared with the one not using data enhancement under the condition of insufficient and unbalanced image datasets.

关键词： Orchid Types classification algorithm Data Enhancement

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classification algorithm sensitivity to training data with non representative attribute noise

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DECISION SUPPORT SYSTEMS 2009年第3期46卷 743-751页

作者： Mannino, Michael Yang, Yanjuan Ryu, Young Univ Colorado Sch Business Denver CO 80217 USA Univ Texas Dallas Sch Management Richardson TX 75083 USA

We present an empirical comparison of classification algorithms when training data contains attribute noise levels not representative of field data. To study algorithm sensitivity, we develop an innovative experimental design using noise situation, algorithm, noise level, and training set size as factors. Our results contradict conventional wisdom indicating that investments to achieve representative noise levels may not be worthwhile. ill general, over representative training noise Should be avoided while under representative training noise is less of a concern. However, interactions among algorithm, noise level, and training set size indicate that these general results may not apply to particular practice situations. (c) 2008 Elsevier B.V. All rights reserved.

关键词： Attribute noise Area under the Receiver Operating Curve classification algorithm

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A Defects classification algorithm for the Hybrid OBT-IDDQ Fault Diagnosis Technique in Analog CMOS Integrated Circuits

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第9期33卷 2450146-2450146页

作者： Mirkovic, Dejan D. Stanojlovic Mirkovic, Milena J. Milic, Miljana L. J. Petrovic, Vladimir Z. Univ Nis Fac Elect Engn Dept Elect Aleksandra Medvedeva 14 Nish 1800 Serbia HDL Design House Bulevar Mihajla Pupina 115G Belgrade 11000 Serbia

In this paper, a robust fault detection methodology for complex analog CMOS integrated filters is presented. It is based on combining the two types of testing methodologies, Oscillation-Based Testing (OBT) and IDDQ testing, i.e., measuring of the power-supply current (I-DD). The proposed methodology is applied to the Bi-quad Sallen-Key band-pass (BP) filter cell with relatively complex, two-stage, class-AB-output, operational amplifier (opamp) topology. The filter is custom designed targeting the 180-nm CMOS technology. Hundreds of time-domain simulations and analyses of the circuit output signal are performed in order to obtain the fault dictionary. The presented results show that the proposed hybrid OBT-IDDQ methodology is significantly more efficient in the defects coverage than any of the particular test methodologies alone. Subsequently, the specific algorithm for the defects classification is proposed. Based on the classification, certain degree of diagnosis of the individual defect, or a group of defects, can be achieved.

关键词： Oscillation-based testing diagnostics classification algorithm analog filters quiescent current

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A new classification algorithm recommendation method based on link prediction

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KNOWLEDGE-BASED SYSTEMS 2018年 159卷 171-185页

作者： Zhu, Xiaoyan Yang, Xiaomei Ying, Chenzhen Wang, Guangtao Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Shaanxi Peoples R China JD AI Res Mountain View CA 94043 USA Changan Univ Xian Shaanxi Peoples R China

Recommending appropriate classification algorithm(s) for a given classification problem is of great significance and also one of the challenging problems in the field of data mining, which is usually viewed as a meta-learning problem. Multi-label learning has been adopted and validated to be an effective meta-learning method in classification algorithm recommendation. However, the multi-label learning method used in previous classification algorithm recommendation relies only on relationship between data sets and their direct neighbours, ignoring the impact of other data sets. In this paper, a new classification algorithm recommendation method based on link prediction between data sets and classification algorithms is proposed. Taking advantage of link prediction in heterogeneous networks, this method considers the impact of all data sets and makes full use of the interactions between data sets as well as between data sets and algorithms. Firstly, meta data of the training data sets is collected. And then a heterogeneous network called DAR (Data and algorithm Relationship) Network is constructed with the meta data. Finally, the link prediction technique is adopted to recommend appropriate algorithm(s) for a given data set on the basis of the DAR Network. To evaluate the proposed link prediction-based recommendation method, extensive experiments with 131 data sets and 21 classification algorithms are conducted. Results of 5 performance measures indicate that the proposed method is more effective compared with the base line classification algorithm recommendation method and can be used in practice.

关键词： classification algorithm Recommendation method Link prediction Heterogeneous network

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Gesture recognition based on an improved local sparse representation classification algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 10935-10946页

作者： He, Yang Li, Gongfa Liao, Yajie Sun, Ying Kong, Jianyi Jiang, Guozhang Jiang, Du Tao, Bo Xu, Shuang Liu, Honghai Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Educ Educ Minist Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Hubei Peoples R China Univ Portsmouth Sch Comp Portsmouth PO1 3HE Hants England

The sparse representation classification method has been widely concerned and studied in pattern recognition because of its good recognition effect and classification performance. Using the minimized l(1) norm to solve the sparse coefficient, all the training samples are selected as the redundant dictionary to calculate, but the computational complexity is higher. Aiming at the problem of high computational complexity of the l(1) norm based solving algorithm, l(2) norm local sparse representation classification algorithm is proposed. This algorithm uses the minimum l(2) norm method to select the local dictionary. Then the minimum l(1) norm is used in the dictionary to solve sparse coefficients for classify them, and the algorithm is used to verify the gesture recognition on the constructed gesture database. The experimental results show that the algorithm can effectively reduce the calculation time while ensuring the recognition rate, and the performance of the algorithm is slightly better than KNNSRC algorithm.

关键词： Gesture recognition l(2) norm Sparse representation classification algorithm

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Library Event Matching event classification algorithm for electron neutrino interactions in the NOνA detectors

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2015年 778卷 31-39页

作者： Backhouse, C. Patterson, R. B. CALTECH Lauritsen Lab Pasadena CA 91125 USA

We describe the Library Event Matching classification algorithm implemented for use in the NO nu A nu(mu)->nu(e), oscillation measurement. Library Event Matching, developed in a different form by the earlier MINOS experiment, is a powerful approach in which input trial events are compared to a large library of simulated events to find those that best match the input event. A key feature of the algorithm is that the comparisons are based on all the information available in the event, as opposed to higher-level derived quantities. The final event classifier is formed by examining the details of the best-matched library events. We discuss the concept, definition, optimization, and broader applications of the algorithm as implemented here. Library Event Matching is well-suited to the monolithic, segmented detectors of NO nu A and thus provides a powerful technique for event discrimination. (C) 2015 Elsevier *** rights reserved.

关键词： Library matching classification algorithm Particle identification NO nu A

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Personalized recommendation of film and television culture based on an intelligent classification algorithm

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PERSONAL AND UBIQUITOUS COMPUTING 2020年第2期24卷 165-176页

作者： Cong, Hongyan Xian Polytech Univ Sch New Media Art Xian 710048 Shaanxi Peoples R China

Personalized recommendation of film and television culture is an important content to meet people's daily cultural needs and social information. Promoting the personalized recommendation of film and television culture is conducive to promoting the more efficient use of network resources. However, in recent years, the film and television culture industry has developed rapidly, and the production of film and television culture has also increased year by year. How to quickly and accurately find the user's favorite film and television culture in the massive film and television cultural data has become an urgent problem to be solved. Aiming at the shortcomings of the film and television culture recommendation system, this paper proposes a new personalized recommendation algorithm for film and television culture based on an intelligent classification algorithm. Based on the preliminary screening results of the traditional collaborative filtering recommendation algorithm, the user data and video data are used as input and the video score as output, which is further filtered by a convolutional neural network. Finally, selecting the film and television culture recommendation set that is most suitable for the current user can also make up for the cold start problem of collaborative filtering at the beginning of the system operation. The simulation experiment is carried out. The experimental results show that the personalized recommendation algorithm based on an intelligent classification algorithm improves the scoring accuracy by 0.15, which indicates that the designed film and television culture recommendation system has a good application effect.

关键词： Film and television culture Personalized recommendation classification algorithm Convolutional neural network

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Evaluation of chicken freshness using a low-cost colorimetric sensor array with AdaBoost-OLDA classification algorithm

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LWT-FOOD SCIENCE AND TECHNOLOGY 2014年第2期57卷 502-507页

作者： Chen, Quansheng Hui, Zhe Zhao, Jiewen Ouyang, Qin Jiangsu Univ Sch Food & Biol Engn Zhenjiang 212013 Peoples R China

This paper attempted to evaluate chicken freshness using a low-cost colorimetric sensor array with the help of a classification algorithm. We fabricated a novel and low-cost colorimetric sensors array, with a specific colorific fingerprint to volatile compounds, using printing chemically responsive dyes on a C2 reverse silica-gel flat plate. In addition, we proposed a novel classification algorithm for sensors data classification - orthogonal linear discriminant analysis (OLDA) and adaptive boosting (AdaBoost) algorithm, namely AdaBoost-OLDA. And we compared it with two classical classification algorithms - linear discriminant analysis (LDA) and back propagation artificial neural network (BP-ANN). Experimental results showed classification results by AdaBoost-OLDA algorithm is superior to BP-ANN and LDA algorithms, the classification results by which are both 100% in the calibration and prediction sets. This study sufficiently demonstrated that the colorimetric sensors array with a classification algorithm has a high potential in evaluating chicken freshness, and AdaBoost-OLDA algorithm has a strong performance in solution to a complex data classification. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Colorimetric sensor array classification algorithm Chicken Freshness

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A New Neural Dynamic classification algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017年第12期28卷 3074-3083页

作者： Rafiei, Mohammad Hossein Adeli, Hojjat Ohio State Univ Dept Civil Environm & Geodet Engn Columbus OH 43210 USA

The keys for the development of an effective classification algorithm are: 1) discovering feature spaces with large margins between clusters and close proximity of the classmates and 2) discovering the smallest number of the features to perform accurate classification. In this paper, a new supervised classification algorithm, called neural dynamic classification (NDC), is presented with the goal of: 1) discovering the most effective feature spaces and 2) finding the optimum number of features required for accurate classification using the patented robust neural dynamic optimization model of Adeli and Park. The new classification algorithm is compared with the probabilistic neural network (PNN), enhanced PNN (EPNN), and support vector machine using two sets of classification problems. The first set consists of five standard benchmark problems. The second set is a large benchmark problem called Mixed National Institute of Standards and Technology database of handwritten digits. In general, NDC yields the most accurate classification results followed by EPNN. A beauty of the new algorithm is the smoothness of convergence curves which is an indication of robustness and good performance of the algorithm. The main aim is to maximize the prediction accuracy.

关键词： classification algorithm Mixed National Institute of Standards and Technology (MNIST) neural dynamic model of Adeli and Park probabilistic neural networks (PNNs) supervised learning

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A sensitized variable selection control chart based on a classification algorithm for monitoring high-dimensional processes

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第7期39卷 2837-2850页

作者： Zhang, Shuai Xue, Li He, Zhen Liu, Yumin Xin, Zhongyuan Henan Univ Engn Sch Management Engn Zhengzhou Henan Peoples R China Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou Henan Peoples R China Tianjin Univ Coll Management & Econ Tianjin Peoples R China Zhengzhou Univ Sch Business Zhengzhou Henan Peoples R China Xiaomi Technol Singapore Pte Ltd Dept Int Internet Business Singapore Singapore 15 Wenyuan West Rd Zhengzhou Peoples R China

During the last decade, variable-selection-based (VS) control charts have gained much popularity for process monitoring and diagnosis. These charts have been proven efficient for the detection of sparse mean shifts in high-dimensional processes. VS charts usually assume that in-control (IC) data are the only information used to determine the control limits. In modern industrial processes, however, out-of-control (OC) data can be easily collected. Detecting a specific shift in a data-rich environment without utilizing OC data information will limit the development of a process monitoring scheme. In this paper, a novel variable selection control chart that is combined with a classification algorithm is proposed, which is expected to benefit from both the classification and variable selection approaches. In contrast to alternative charts, the proposed sensitized variable selection chart can capture the potential shifted variables using both IC and OC information, which can improve the sensitivity of the chart in a specific direction. Extensive Monte Carlo simulations demonstrate that the proposed chart outperforms the alternatives in a data-rich and high-dimensional environment. A real-life example of cellular localization is also included to support the findings of our study.

关键词： classification algorithm high-dimensional process statistical process control variable selection

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