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检索条件"主题词=Classification algorithm"

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Deriving the Relationship Between User Satisfaction on Engine Sounds and Affective Variable Sets Based on classification algorithms

Deriving the Relationship Between User Satisfaction on Engin...

引用

IEEE International Conference on Industrial Engineering and Engineering Management

作者： Kim, W. J. Kim, G. W. Lee, Y. S. Yun, M. H. Seoul Natl Univ Dept Ind Engn Seoul South Korea

ISBN: (纸本)9781479964109

This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the prediction accuracy for those affective axes. The study was consisted of three phases: 1) extracting sets of affective variables and the level of satisfaction on engine sounds, 2) preprocessing of engine sounds and experiment design, and 3) analysis of the most relevant sets of affective variables to user satisfaction. As a result, PA (Powerful-Affective) variable set showed the highest prediction accuracy of user satisfaction compared to other sets. Predicting the level of satisfaction based on classification algorithm could help to generalize the relationship between user satisfaction and affective variables more easily, beyond the limitation with a small size of subjects.

关键词： Emotion prediction product sounds classification algorithm user satisfaction engine sounds

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Using ABC algorithm for classification and Analysis on Effects of Control Parameters

Using ABC Algorithm for Classification and Analysis on Effec...

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18th National Biomedical Engineering Meeting (BIYOMUT)

作者： Dilmac, Selim Korurek, Mehmet Istanbul Tech Univ Fen Bilimleri Enstitusu Maslak Sariyer Is Turkey

ISBN: (纸本)9781479975723

In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH ECG database. In addition to it, validity indices and effects of some control parameters such as MCN, Limit are examined. It is observed that, selected features have significiant effect on classification success rate of classifier. If there is high overlap between the classes, success rate of classifier decreases. However observed results indicate that ABC algorithm can successfully be used for classification of multi dimensional datasets. By means of SCTR control parameter, MABC algorithm based classifier provides higher classification success rates versus ABC algorithm, independent from Limit and MCN values.

关键词： classification data analysis electrocardiography feature extraction feature selection learning (artificial intelligence) medical information systems medical signal processing signal classification swarm intelligence ABC algorithm effectiveness BUPA IRIS MABC algorithm based classifier MCN parameter MITBIH ECG database PIMA SCTR control parameter UCI Machine Learning Repository WINE analysis algorithm artificial bee colony algorithm based classifier class overlap classification algorithm classification success rate control parameter effect feature effect limit parameter multidimensional dataset classification validity index classification algorithms Educational institutions Electrocardiography Indexes Iris Machine learning algorithms Particle swarm optimization Feature extraction Sorting algorithm Electrocardiography Particle swarm optimization Wine medical information systems control parameter INTEGRATED RISK INFORMATION SYSTEM Machine learning algorithms medical signal processing signal classification Educational institutions Classifiers activity based costing Iris

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Efficient Approach for Epileptic Seizure classification and Detection based on Genetic algorithm with CNN-RNN Classifier 3

Efficient Approach for Epileptic Seizure Classification and ...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Priyanka, R. Sathesh, M. Selvi, Tamil T. Saikumar, P. Janardhan Venkatachalam, K. Raja, M. SRM Inst Sci & Technol Sch Comp Dept Networking & Commun Coll Engn & Technol KTR Campus Chengalpattu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept ECE Chennai Tamil Nadu India Sri Sai Ram Engn Coll Dept Elect & Instrumentat Engn Chennai Tamil Nadu India Audi Sankara Coll Engn & Technol Dept ECE Gudur India Audi Sankara Coll Engn & Technol Gudur Dept Elect & Commun Engn Gudur India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept ECE Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

One of the most physically and psychologically damaging neurological conditions that affect people of all ages is an epileptic seizure. The abnormality should be recognized early so that the proper treatment is timely. It is possible only with advanced signal processing techniques to distinguish and predict epileptic patterns in which substantial effort is invested. Therefore, efficient seizure detection and classification methods are proposed for machine learning and deep learning algorithms. The proposed method uses deep and machine-learning algorithms for seizure detection and classification. The objective is to analyze the performance and efficiency of deep and machine learning classifiers by comparing the various classifiers. This proposed work uses 11,500 EEG data samples from the UCI machine learning repository. To suggest an Improved Fitness Function Genetic algorithm (IGA) technique for optimal feature selection to improve the detection rate and CNN-RNN algorithm used as the classifier. The analysis proved that the hybrid CNN-RNN(LSTM with GRU) classifier with GA-based feGA-based-lection provides better densification accuracy results of 98% when compared with all other classifiers.

关键词： Epileptic Seizures Genetic algorithm Convolutional Neural Network Recurrent Neural Network classification algorithm

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Comparison of Five Common Land Cover Supervised classification algorithms Based on GF-2 and Landsat8 Data 12

Comparison of Five Common Land Cover Supervised Classificati...

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12th International Conference on Signal Processing Systems

作者： Li, Jiakun Huang, Jianhua Chen, Xiaomao Bai, Yang Shi, Huien Xiao, Yu Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)9781510642768;9781510642751

With the development of remote sensing technology and the differences in remote sensing image classification, it is particularly important to be able to accurately use classification methods to classify images and to compare classification algorithms. In this paper, taking Yangshuo County as the research area, five common supervised classifications, namely support vector machine (SVM), maximum likelihood classification (MLC), neural network (NN), spectral angle mapping (SAM) and spectral information divergence (SID), are used to classify the land cover of remote sensing image data of GF-2. Landsat8 and its fusion in the same area. The classification results are obtained and compared. Moreover, the overall classification accuracy (OA) and Kappa coefficient are used to evaluate the performance of the image classification algorithm. The results show that both MLC and SVM perform best on these three data sets. For higher spatial resolution GF-2 and fusion data, the OA and Kappa coefficients of both image data classifiers is 10% higher than those of Landsat8 data with higher spectral resolution.

关键词： Remote sense land cover classification classification algorithm overall classification accuracy kappa coefficients

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Skin Anomaly Detection Using classification algorithms 25

Skin Anomaly Detection Using Classification Algorithms

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IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME)

作者： Andronescu, A. D. Nastac, D. I. Tiplica, G. S. Univ Politehn Bucuresti Fac Elect Telecommun & Informat Technol Bucharest Romania Carol Davila Univ Med & Pharm Colentina Hosp Bucharest Romania

ISBN: (纸本)9781728133300

This paper proposes a solution for the most easily detectable type of cancer, skin cancer. Due to the ozone layer thinning, it was observed an increase in the number of skin lesion anomalies. Those anomalies are produced by the increasing UV radiation intensity. Good protection against sun is not always facile, mainly in the exposed regions of the skin like the face and the lower regions of the arms. Most types of cancers can be easily extracted via surgery if they are detected in one of the incipient development stages. Using a high resolution image, a classification algorithm can detect certain patterns in the nevi color, shape, veins or irregularities inside. Analyzing those aspects, a person can track their nevi development easily and intervene if it necessary. The originality of this paper consist in designing several convolutional neural networks, using the Python programming language, the Keras API and the Tensorflow framework alongside with proper dataset selection. The convolutional neural networks were trained using an open source dataset, with the purpose of formulating a diagnosis for new patients. The convolutional neural networks are provided with 7 classes of images, representing 7 types of skin lesions as input, and will output a diagnosis. The result of the research was a classifier with a convenient classification accuracy based on the dataset, and a medium accuracy based on new data.

关键词： skin cancer nevi classification algorithm artificial neural network

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Research on Feature Extraction and classification algorithms for Infrared Targets 17

Research on Feature Extraction and Classification Algorithms...

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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Yang, Xuqiang Li, Yanbin Zhang, Yan Yang, Chunling Li, SuYing Harbin Inst Technol Sch Elect Egineering Harbin Peoples R China

ISBN: (纸本)9781665409841

Infrared target detection techniques have been widely used in infrared alarming and reconnaissance, and the detection task is often done by machine learning. Feature extraction and classification algorithm are two core components of the machine learning. The selection of features and algorithm have a determinative effect on the detection result. Traditional target classification techniques only focus on only limited combinations of features and classification algorithm, which may result in a poor detection result. Based on this, this paper selects the gray features, statistical features, frequency domain features, and graphic features of the infrared target, and compares the three classification algorithms of KNN, Bayes, and SVM to give the optimal combination of features and classification algorithms by experiment.

关键词： Infrared target detection Feature extraction classification algorithm

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Emotional Text Analysis Based on Ensemble Learning of Three Different classification algorithms 10

Emotional Text Analysis Based on Ensemble Learning of Three ...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Bian, WenShuo Wang, ChunZhi Ye, ZhiWei Yan, Lingyu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781728140681

In order to improve the accuracy and generalization performance of text sentiment analysis model, an integrated learning model is proposed in this paper, which includes three different classification algorithms - Logistic regression, support vector machine and K-Neighborhood algorithm. Compared with single classification algorithm, this algorithm shows better accuracy. The experimental results show that the model has good generalization performance and robustness.

关键词： text emotional analysis classification algorithm Ensemble learning generalization performance

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Effective classification algorithms and Feature Selection for Bio-Medical Data using IoT 7

Effective Classification Algorithms and Feature Selection fo...

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7th International Conference on Information Technology Trends (ITT)

作者： Masood, Junaid Shahzad, Muhammad Khan, Zahoor Ali Akre, Vishesh Rajan, Amala Ahmed, Sheeran Masood, Fawad Iqra Natl Univ Peshawar Peshawar Pakistan MNS Univ Engn & Technol Multan Pakistan Career Dynam Res Ctr Peshawar Pakistan Higher Coll Technol CIS Fujairah U Arab Emirates Higher Coll Technol Dubai U Arab Emirates Inst Space Technol Islamabad Pakistan

ISBN: (纸本)9781728183787

Data mining is the process to predict future trends. Data mining involving searching of patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it into an understandable structure for later use. Data mining using IoT becomes one of the leading providers applicable in several areas. The method of extracting usable data from larger set of arbitrary raw data is called data mining. This includes the analysis of sample data in large amounts of data with one or more programs. Concentrate on large amounts of data and databases to analyze, we use predictive analysis in the field of medicine. In this study, we examine the performance of algorithms alongwith the use of IoT that analyses large amounts of scattered information to make sense of it and turn it into knowledge. For this we used feature selection and classification algorithm on hepatitis data to get best result with minimum error rate and compare feature selection with classification algorithm so that to check which method is best according to results.

关键词： Data Mining classification algorithm IoT Feature Selection algorithm Error rate

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Color Naming for Describing Object in Image using Different classification algorithms and Color Spaces 2020

Color Naming for Describing Object in Image using Different ...

引用

12th International Conference on Computer and Automation Engineering (ICCAE)

作者： Sainui, Janya Tongsamrit, Mananya Prince Songkla Univ Fac Sci Dept Comp Sci Hat Yai Thailand

ISBN: (纸本)9781450376785

In this paper, we study to name a color of an object in the given image, where the number of color names is 11 English basic color names, including pink, purple, red, orange, yellow, green, blue, gray, brown, black, and white. Our goal is to apply machine learning algorithms for assigning a color name to a principal object in images. Here, we evaluate three basic classification algorithms, namely Support Vector Machine (SVM), naive Bayes, and k-Nearest Neighbor (k-NN). The feature vector of the object in an image is the pixel intensities represented in different color spaces including RGB, HSV, Lab and CYMK. We randomly collect the training pixel intensities with their corresponding color names from the images obtained from the Internet. We then evaluate three classification algorithms using the e-Bay dataset. The experimental results show that the linear SVM with Lab color space is the best choice for solving this task.

关键词： Color naming classification algorithm support vector machine k-nearest neighbor naive Bayes color spaces

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A Comparative Study of Rule Based classification algorithms for Credit Risk Assessment 31

A Comparative Study of Rule Based Classification Algorithms ...

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31st International-Business-Information-Management-Association Conference

作者： Smiti, Salima Soui, Makram Gasmi, Ines Univ Gabes Gabes Tunisia Cent Univ Grp Interdisciplinary Res Ctr Gabes Tunisia Univ Manouba Manouba Tunisia

ISBN: (纸本)9780999855102

Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge several evaluations techniques are black-box models such as neural networks, SVM, etc. They generate applicants' classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules, which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: Accuracy, Sensitivity and Specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict credit risk.

关键词： classification algorithm credit risk assessment data mining rule extraction

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