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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Mittal, Vikas Singh, D. Saini, L. M. NIT Kurukshetra ECE Dept Kurukshetra Haryana India IIT Roorkee Elect & Comm Engn Dept Uttarakhand India NIT Elect Engn Dept Kurukshetra Haryana India

ISBN: (纸本)9781479964994

Complementary information contained in each of the individual polarization mode of fully polarimetric PALSAR data is fused using Expectation Maximization (EM) algorithm to improve information contents in the fused image. A comparative analysis of supervised classifiers viz. parallelepiped and minimum distance is accomplished to assess the suitability of the particular classifier. It is also demonstrated that fusion indeed improves various figures of merit such as producer, user and overall accuracies.

关键词： PALSAR image data Expectation maximization method multi-polarized image fusion land cover classification supervised classification algorithms

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Driver identification in intelligent vehicle systems using machine learning algorithms

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第1期13卷 40-47页

作者： Li, Zhengping Zhang, Kai Chen, Bokui Dong, Yuhan Zhang, Lin Tsinghua Univ Div Logist & Transportat Grad Sch Shenzhen Shenzhen Peoples R China Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Tsinghua Univ Div Informat Sci & Technol Grad Sch Shenzhen Shenzhen Peoples R China Tsinghua Berkeley Shenzhen Inst Data Sci & Informat Technol Res Ctr Shenzhen Peoples R China

This study proposes an applicable driver identification method using machine learning algorithms with driving information. The driving data are collected by a 3-axis accelerometer, which records the lateral, longitudinal and vertical accelerations. In this research, a data transformation way is developed to extract interpretable statistics features from raw 3-axis sensor data and utilise machine learning algorithms to identify drivers. To eliminate the bias caused by the sensor installation and ensure the applicability of their approach, they present a data calibration method which proves to be necessary for a comparative test. Four basic supervised classification algorithms are used to perform on the data set for comparison. To improve classification performance, they propose a multiple classifier system, which combines the outputs of several classifiers. Experimental results based on real-world data show that the proposed algorithm is effective on solving driver identification problem. Among the four basic algorithms, random forests (RFs) algorithm has the greatest performance on accuracy, recall and precision. With the proposed multiple classifier system, a greater performance can be achieved in small number of drivers' groups. RFs algorithm takes the lead in running speed. In their experiment, ten drivers are involved and over 5,500,000 driving records per driver are collected.

关键词： learning (artificial intelligence) intelligent transportation systems feature extraction traffic engineering computing image classification calibration statistical analysis RFs algorithm random forests algorithm multiple classifier system supervised classification algorithms data calibration method sensor installation raw 3-axis sensor data interpretable statistics feature extraction data transformation lateral accelerations longitudinal accelerations vertical accelerations 3-axis accelerometer driving data collection machine learning algorithms intelligent vehicle systems driver identification method

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Implementation of a classification strategy of Raman data collected in different clinical conditions: application to the diagnosis of chronic lymphocytic leukemia

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2020年第4期412卷 949-962页

作者： Fere, M. Gobinet, C. Liu, L. H. Beljebbar, A. Untereiner, V. Gheldof, D. Chollat, M. Klossa, J. Chatelain, B. Piot, O. Univ Reims Fac Pharm BioSpecT EA 7506 51 Rue Cognacq Jay F-51096 Reims France Univ Reims Fac Pharm Cellular Tissular Imaging Platform PICT 51 Rue Cognacq Jay F-51096 Reims France Catholic Univ Louvain CHU UCL Namur Namur Thrombosis & Hemostasis Ctr Hematol Lab Rue Dr Gaston Therasse B-5530 Yvoir Belgium TRIBVN 39 Rue Louveau F-92320 Chatillon France

The literature is rich in proof of concept studies demonstrating the potential of Raman spectroscopy for disease diagnosis. However, few studies are conducted in a clinical context to demonstrate its applicability in current clinical practice and workflow. Indeed, this translational research remains far from the patient's bedside for several reasons. First, samples are often cultured cell lines. Second, they are prepared on non-standard substrates for clinical routine. Third, a unique supervised classification model is usually constructed using inadequate cross-validation strategy. Finally, the implemented models maximize classification accuracy without taking into account the clinician's needs. In this paper, we address these issues through a diagnosis problem in real clinical conditions, i.e., the diagnosis of chronic lymphocytic leukemia from fresh unstained blood smears spread on glass slides. From Raman data acquired in different experimental conditions, a repeated double cross-validation strategy was combined with different cross-validation approaches, a consensus label strategy and adaptive thresholds able to adapt to the clinician's needs. Combined with validation at the patient level, classification results were improved compared to traditional strategies.

关键词： Raman spectroscopy Chronic lymphocytic leukemia Pre-processing supervised classification algorithms Label consensus Clinical practice

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classification of textured images based on new information fusion methods

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IET IMAGE PROCESSING 2019年第9期13卷 1540-1549页

作者： Zitouni, Abdelkader Benkouider, Fatiha Chouireb, Fatima Belkheiri, Mohammed Univ Amar Telidji Laghouat Dept Elect Laghouat Algeria Univ Amar Telidji Laghouat LTSS Laghouat Algeria

This work presents supervised classification algorithms based on information fusion for textured-images segmentation. Gabor features are efficient in finding class boundaries, whereas grey-Level co-occurrence matrix features are favourable in the areas within the classes. Moreover, the wavelets can represent textures at different scales and offer great discriminatory power between textures with strong resemblances. This motivates us to combine these three kinds of features with improving image segmentation. In the first step, the proposed method applied those three feature extraction strategies on textured images to get more information. After that in the second step, the estimated feature vector of each pixel is sent to the neural networks classifier for pre-labelling. Then, in the third step of the proposed method, a classifier fusion method used to combine the scores obtained by the neural networks for each pixel. Finally, in the last step, to obtain more precise segmentation results, the scores within a sliding window are combined. The performance of the proposed segmentation algorithms was evaluated on synthetic images from Brodatz and DTD datasets. The obtained classification results from the proposed fusions system lead to higher classification precision compared to applying a single classifier on the textured images.

关键词： image classification image segmentation neural nets feature extraction image texture image fusion information fusion methods supervised classification algorithms textured-images segmentation estimated feature vector neural networks classifier classifier fusion method synthetic images Gabor features grey-level co-occurrence matrix textured image classification feature extraction sliding window Brodatz datasets DTD datasets

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Improving Medical Diagnosis Reliability Using Boosted C5.0 Decision Tree empowered by Particle Swarm Optimization 37

Improving Medical Diagnosis Reliability Using Boosted C5.0 D...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Pashaei, Elnaz Ozen, Mustafa Aydin, Nizamettin Yildiz Tech Univ Dept Comp Engn Istanbul Turkey Istanbul Univ Istanbul Turkey

ISBN: (纸本)9781424492701

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

关键词： decision trees learning (artificial intelligence) medical diagnostic computing particle swarm optimisation pattern classification reliability UCI machine learning databases biomedical applications boosted C4.5 decision tree classifier boosted C5.0 decision tree fitness function five-fold cross-validation method medical data sets medical diagnosis reliability microarray dataset particle swarm optimization supervised classification algorithms Accuracy Boosting classification algorithms Decision trees Particle swarm optimization Sensitivity Support vector machines

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Predictive Modelling of Customer Sustainable Jewelry Purchases Using Machine Learning algorithms

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Procedia Computer Science 2024年 235卷 683-700页

作者： Anjali Munde Jasmandeep Kaur Southampton Malaysia Business School University of Southampton Malaysia Ideal Institute of Management and Technology Delhi India

In order to give businesses useful information, the main goal of this study is to forecast future consumer purchases in the sustainable jewelry sector. Companies launching new goods, services, or modifications to their current offers must anticipate client behaviours. Businesses can learn a lot about how current customers behave by studying their behaviour in order to develop marketing and sales techniques that work. The study uses a variety of statistical and machine learning methods to forecast the most important elements influencing the purchasing of sustainable jewelry in order to achieve this. These variables span a wide range of characteristics, including gender, age, educational attainment, occupation, gross monthly income, marital status, previous jewelry purchase history, purchase intention, preferred shopping locations, online purchase influencers, consumer buying patterns, preferences for branded and non-branded jewelry, and participation in sustainable jewelry practices. In this study, supervised classification algorithms such Naive Bayes, Support Vector Machine, Linear Discriminant Analysis, K-Nearest Neighbor, and Decision Tree are compared over a wide range of machine learning approaches. To increase the predictive model's effectiveness and resilience, ensemble learning techniques including Random Forest, AdaBoost, and Bagging Classifiers are also introduced. A confusion matrix and classification report are the performance indicators used to assess these models. When used on a test dataset, these measures evaluate the model's precision, recall, accuracy, and F1 scores. These measurable metrics are essential for evaluating the models' reliability and efficacy. Notably, the study shows that the approaches of Random Forest, Decision Tree, Bagging Classifier, and Linear Discriminant Analysis continuously produce the highest accuracy rates, all of which stand at a remarkable 100% accuracy. Other used machine learning classifiers, such as Naive Bayes, K-Nea

关键词： Sustainable Jewelry purchase prediction Machine learning supervised classification algorithms Predictive Modelling Confusion Matrix classification report

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