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Lane Change Intention classification of Surrounding Vehicles Utilizing Open Set Recognition

IEEE ACCESS 2021年 9卷 57589-57602页

作者： Kim, Dae Jung Kim, Jin Sung Yang, Jin Ho Kee, Seok Cheol Chung, Chung Choo Hanyang Univ Dept Elect Engn Seoul 04763 South Korea Chungbuk Natl Univ Smart Car Res Ctr Cheongju 28644 South Korea Hanyang Univ Div Elect & Biomed Engn Seoul 04763 South Korea

This paper proposes a classification algorithm utilizing an open set recognition concept to conservatively detect lane change intention of surrounding vehicles. Conservatively predicting the lane change intention of the surrounding vehicles is needed to improve adaptive cruise control (ACC) performance and avoid possible accidents. However, existing machine learning can make incorrect decisions due to information not included in the training data set or confused data even with probability. To cope with this problem, we present a classification algorithm using a multi-class support vector machine applying an open set recognition concept to detect the surrounding vehicles' lane change intentions. Feature vectors are constructed from lateral information obtained by a Kalman filter using only radar and in-vehicle sensors. The open set recognition concept is adapted using Meta-Recognition based on binary classifiers scores. Furthermore, we analyze lateral information where an object vehicle changes lanes. From experimental results, we observe that the proposed system conservatively deals with wrong decisions and detects and cancels detecting the closest in-path vehicle (CIPV) earlier with average times of 1.4 sec and 0.4 sec compared with a commercial radar system, respectively.

关键词： Radar Feature extraction Radar detection Sensors Predictive models Training data Support vector machines Advanced driver assistance system lane change intention surrounding vehicles machine learning open set recognition classification algorithm multi-class support vector machine

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Machine Learning Framework for Multi-Level classification of Company Revenue

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IEEE ACCESS 2021年 9卷 96739-96750页

作者： Choi, Jung-Gu Ko, Inhwan Kim, Jeongjae Jeon, Yeseul Han, Sanghoon Yonsei Univ Yonsei Grad Program Cognit Sci Seoul 03722 South Korea Yonsei Univ Dept Psychol Seoul 03722 South Korea Yonsei Univ Dept Appl Stat Seoul 03722 South Korea

The planning and execution of a business strategy are important aspects of the strategic human resource management of a company. In previous studies, machine learning algorithms were used to determine the main factors correlating employees with company performance. In this study, we introduced a method based on machine-learning algorithms for the classification of company revenue. Both annual and integrated datasets were examined to evaluate the classification performance of the framework under both binary and multiclass conditions. The performance of the proposed method was validated using six evaluation metrics: accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve. As the experimental results indicate, the XGBoost classifier displayed the best classification performance among the three algorithms (XGBoost classifier, stochastic gradient descent classifier, and logistic regression) used in this study. Moreover, we confirmed the important features of the trained XGBoost model in accordance with variables focusing on human resource management studies. These results demonstrate that the proposed framework has strength in terms of both classification and practical implementation. This study provides novel insights into the relationship between employees and the revenue levels of their employer.

关键词： Companies Machine learning classification algorithms Machine learning algorithms Feature extraction Human resource management Data models Machine learning company revenue level classification algorithm human resource management

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classification modeling based on surface porosity for the grading of natural cork stoppers for quality wines

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FOOD AND BIOPRODUCTS PROCESSING 2015年 93卷 69-76页

作者： Oliveira, Vanda Knapic, Sofia Pereira, Helena Univ Lisbon Inst Super Agron CEF P-1349017 Lisbon Portugal

The natural cork stoppers are commercially graded into quality classes according with the homogeneity of the external surface. The underlying criteria for this classification are subjective without quantified criteria and standards defined by cork industry or consumers. Image analysis was applied to premium, good and standard quality classes to characterize the surface of the cork stoppers and stepwise discriminant analysis (SDA) was used to build predictive classification models. The final goal is to analyze the contribution of each porosity feature and propose an algorithm for cork stoppers quality class classification. This study provides the knowledge based on a large sampling to an accurate grading of natural cork stoppers. In average all the models presented accuracy in relation to the commercial classification over 68% with a higher mismatch in the mid-quality range. Color showed an important discriminating power, increasing the accuracy in 10%. The main discriminant features were porosity coefficient and color variables, calculated for the lateral surface. A quality classification algorithm was presented based on a simplified model with an accuracy of 75%. The classification based on color vision systems can ensure improved quality class uniformity and a higher transparency in trade. (C) 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Natural cork stoppers Quality classes Image analysis Porosity Discriminant analysis classification algorithm

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classification of human gingival sulcus using swept-source optical coherence tomography: In vivo imaging

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INFRARED PHYSICS & TECHNOLOGY 2019年第0期98卷 155-160页

作者： Lee, Jaeyul Park, Jaeseok Shirazi, Muhammad Faizan Jo, Hosung Kim, Pilun Wijesinghe, Ruchire Eranga Jeon, Mansik Kim, Jeehyun Kyungpook Natl Univ Coll IT Engn Sch Elect Engn 80 Daehak Ro Daegu 41566 South Korea Med Univ Vienna Ctr Med Phys & Biomed Engn Waehringer Guertel 18-20 A-1090 Vienna Austria Kyungpook Natl Univ Inst Biomed Engn Sch Med 680 Gukchaebosang Ro Daegu 41944 South Korea Kyungil Univ Dept Biomed Engn Coll Engn 50 Gamasil Gil Gyongsan 38428 Gyeongsangbuk D South Korea

We demonstrated a preliminary research to investigate the feasible in vivo utilization of swept-source optical coherence tomography (SS-OCT) system with 1310 nm wavelength band to obtain morphological visualizations and human gingival sulcus depth measurements. Apart from the cross-sectional analysis, pixel intensity based OCT image classification algorithm is developed to identify the depth of gingival sulcus quantitatively. A total of 43 sites from the periodontal tissues of five healthy individuals were imaged in vivo by using the OCT system. Two periodontal tissues were right and left maxillary central incisors, while the other four periodontal tissues were left and right mandibular central incisors and later incisors. The developed classification algorithm could measure the gingival sulcus depths, which are 1.15 +/- 0.21 mm of the maxilla and 1.06 +/- 0.27 mm of the mandible. The averaged total depths obtained by the system was 1.10 +/- 0.26 mm. Hence, the gingival sulcus depth could be quantitatively measured by using the swept-source OCT system with the developed image classification algorithm as well as revealing a structural visualization, which ultimately confirmed the potential applicability for gingival sulcus depth real-time assessment.

关键词： Optical coherence tomography Swept-source optical coherence tomography classification algorithm Gingival sulcus Periodontal tissue

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An assessment of the effectiveness of decision tree methods for land cover classification

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REMOTE SENSING OF ENVIRONMENT 2003年第4期86卷 554-565页

作者： Pal, M Mather, PM Univ Nottingham Sch Geog Nottingham NG7 2RD England

Choice of a classification algorithm is generally based upon a number of factors, among which are availability of software, ease of use, and performance, measured here by overall classification accuracy. The maximum likelihood (ML) procedure is, for many users, the algorithm of choice because of its ready availability and the fact that it does not require an extended training process. Artificial neural networks (ANNs) are now widely used by researchers, but their operational applications are hindered by the need for the user to specify the configuration of the network architecture and to provide values for a number of parameters, both of which affect performance. The ANN also requires an extended training phase. In the past few years, the use of decision trees (DTs) to classify remotely sensed data ha's increased. Proponents of the method claim that it has a number of advantages over the ML and ANN algorithms. The DT is computationally fast, make no statistical assumptions, and can handle data that are represented on different measurement scales. Software to implement DTs is readily available over the Internet. Pruning of DTs can make them smaller and more easily interpretable, while the use of boosting techniques can improve performance. In this study, separate test and training data sets from two different geographical areas and two different sensors-multispectral Landsat ETM+ and hyperspectral DAIS-are used to evaluate the performance of univariate and multivariate DTs for land cover classification. Factors considered are: the effects of variations in training data set size and of the dimensionality of the feature space, together with the impact of boosting, attribute selection measures, and pruning. The level of classification accuracy achieved by the DT is compared to results from back-propagating ANN and the ML classifiers. Our results indicate that the performance of the univariate DT is acceptably good in comparison with that of other classifiers, except wit

关键词： decision tree land cover classification algorithm

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Intelligent identification of effective reservoirs based on the random forest classification model

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JOURNAL OF HYDROLOGY 2020年 591卷 125324-125324页

作者： Li, Jieyu Zhong, Ping-an Yang, Minzhi Zhu, Feilin Chen, Juan Liu, Weifeng Xu, Sunyu Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili 1 Xikang Rd Nanjing 210098 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China

In the real-time operation of a flood control system, identifying effective reservoirs accurately and adaptively is the premise of establishing a mull-reservoir real-time flood control hybrid operation (MRFCHO) model. The existing effective reservoir intelligent reasoning (ERIK) method is based on the inference rules, which causes the identification results to be greatly influenced by the training samples. This paper establishes a random forest classification (RFC) model for identifying effective reservoirs to solve this problem. The performance of the RFC model is evaluated in terms of model stability and model accuracy, and compared with the ERIK model and other machine learning (ML) models. First, different flood samples are used to establish the models to verify the model stability. Then, the expected total cost under cross-validation is taken as the index to evaluate the classification accuracy. Finally, the average increase of expected total cost with cross-validation is proposed to evaluate the criteria importance and analyze the sensitive factors that affect the classification accuracy of the RFC model. The proposed method is applied to a mull-reservoir system in the Huaihe River basin in China. The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability. It displays more dominance in the dynamic identification of effective reservoirs compared to other models.

关键词： Multi-reservoir system Flood control operation Effective reservoir identification classification algorithm Random forest

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A cluster-based intelligence ensemble learning method for classification problems

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INFORMATION SCIENCES 2021年 560卷 386-409页

作者： Cui, Shaoze Wang, Yanzhang Yin, Yunqiang Cheng, T. C. E. Wang, Dujuan Zhai, Mingyu Dalian Univ Technol Sch Econ & Management Dalian 116023 Peoples R China Univ Elect Sci & Technol China Sch Econ & Management Chengdu 610064 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

classification is a vital task in machine learning. By learning patterns of samples of known categories, the model can develop the ability to distinguish the categories of samples of unknown categories. Noticing the advantages of the clustering method in cluster structure analysis, we combine the clustering and classification methods to develop the novel cluster-based intelligence ensemble learning (CIEL) method. We use the clustering method to analyze the inherent distribution of the data and divide all the samples into clusters according to the characteristics of the dataset. Then, for each specific cluster, we use differ-ent classification algorithms to establish the corresponding classification model. Finally, we integrate the prediction results of each base classifier to form the final prediction result. In view of the problem of parameter sensitivity, we use a swarm intelligence algorithm to optimize the key parameters involved in the clustering, classification, and ensemble stages in order to boost the classification performance. To assess the effectiveness of CIEL, we per -form tenfold cross-validation experiments on the 24 benchmark datasets provided by UCI and KEEL. Designed to improve the performance of the classifiers, CIEL outperforms other popular machine learning methods such as naive Bayes, k-nearest neighbors, random for -est, and support vector machine. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Ensemble learning classification algorithm Combination strategy Clustering algorithm Swarm intelligence algorithm

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classification of dry age-related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary learning

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IET IMAGE PROCESSING 2020年第8期14卷 1571-1579页

作者： Mousavi, Elahe Kafieh, Rahele Rabbani, Hossein Isfahan Univ Med Sci Sch Adv Technol Med Student Res Comm Esfahan Iran Isfahan Univ Med Sci Sch Adv Technol Med Med Image & Signal Proc Res Ctr Esfahan Iran

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are the major causes of vision loss in developed countries. Alteration of retinal layer structure and appearance of exudates are the most significant signs of these diseases. In this paper, with the aim of automatic classification of DME, AMD, and normal subjects using Optical Coherence Tomography (OCT) images, a dictionary-learning based classification is proposed. The two important issues intended in this approach are avoiding retinal layer segmentation and attempting to mimic the authors' understanding based on normal and abnormal region identifications, considering that the signs of diseases appear in a small fraction of B-Scans. The histogram of oriented gradients feature descriptor was utilized to characterize the distribution of local intensity gradients and edge directions. To capture the structure of extracted features, different dictionary learning-based classifiers are employed. The dataset consists of 45 subjects: 15 patients with AMD, 15 patients with DME, and 15 normal subjects. The proposed classifier leads to an accuracy of 95.13, 100.00, and 100.00% for DME, AMD, and normal OCT images, respectively, only by considering 4% of all B-Scans of a volume, which outperforms the state-of-the-art methods.

关键词： feature extraction vision defects image classification diseases image segmentation eye biomedical optical imaging medical image processing optical tomography dry age-related macular degeneration diabetic macular oedema optical coherence tomography images AMD DME vision loss developed countries retinal layer structure diseases automatic classification classification algorithm retinal layer segmentation normal region identifications abnormal region identifications local intensity gradients dictionary learning-based classifiers normal subjects normal OCT images edge directions extracted features

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Burst-classifier: Automated classification of solar radio burst type II III and IV for CALLISTO spectra using physical properties maximum of solar 24

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ADVANCES IN SPACE RESEARCH 2024年第11期74卷 6104-6123页

作者： Afandi, N. Z. Mohd Umar, R. Sabri, N. H. Safei, S. Monstein, C. Lau, C. C. Zafar, S. N. A. Syed Univ Sultan Zainal Abidin East Coast Environm Res Inst ESERI Kuala Nerus 21300 Terengganu Malaysia Univ Malaysia Terengganu Fac Sci & Marine Environm Adv Nano Mat ANoMa Res Grp Kuala Nerus 21030 Terengganu Malaysia Univ Sultan Zainal Abidin Fac Informat & Comp Besut 22200 Terengganu Malaysia Univ Svizzera italiana USI Ist Rech Solari Aldo & Cele Dacco IRSOL Fac Informat CH-6605 Locarno Switzerland Malaysian Space Agcy MYSA Explorat & Space Sci Div 13 Jalan Tun Ismail Kuala Lumpur 50480 Malaysia

Continuous observation of solar radio bursts (SRBs) throughout the year using the CALLISTO spectrometer generates a huge volume of spectral data. This study introduces a burst-classifier algorithm, which is an automated algorithm, to classify the SRB spectrum into three solar radio bursts, namely Type II (SRBT II), Type III (SRBT III) and Type IV (SRBT IV). The proposed algorithm was designed using four characteristic parameters derived from a collection of training dataset files. The characteristic parameters were derived from the intensity bursts observed on frequency channels and timesteps of the spectrum. This dataset consisted of 50 spectra of SRBT II and SRBT III, along with 40 spectra for SRBT IV, collected during the solar maximum of 2014 (Solar Cycle 24). After observations and analysis of the training dataset, each burst type was set up with a threshold. A training dataset of 80 data spectra from 2013 to 2016 was used to test the algorithm. Accuracy of the proposed algorithm was calculated using the percentage of true positives (TP) and false positives (FP). Findings demonstrate an accuracy of X74 % with 57 out of 80 spectra classified as TP and 23 spectra as FP. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Solar radio burst Burst II Burst III Burst IV CALLISTO classification algorithm

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RBNN application and simulation in big data set classification

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 4467-4475页

作者： Zhang, Qin Wilson, Fred Xinxiang Univ Sch Math & Informat Sci Xinxiang 453000 Henan Peoples R China

Text classification technology, an important basis for text mining and information retrieval, is mainly to determine the text category according to the text content under a predetermined set of categories. Traditional manual text categorization has gradually failed to meet the needs, while automatic text categorization based on artificial intelligence has become an important research direction in the field of natural language processing. To this end, this paper introduced the RBNN-based classification algorithm by considering the high dimensionality, non-linearity and complex correlation between feature items, and the theoretical and feasibility analysis were carried out so as to apply it to text feature dimension reduction. Also, the effects of the distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the classification results were studied. Through the computer simulation experiment, the influence rule of distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the training precision and test accuracy of the classification process were demonstrated in the form of curves, which provides guidance for the application of RBNN in pattern recognition.

关键词： Application and simulation radial basis function neural network big data set classification RBNN classification algorithm

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