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Lymph diseases diagnosis approach based on support vector machines with different kernel functions 9

Lymph diseases diagnosis approach based on support vector ma...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Elshazly, Hanaa Ismail Elkorany, Abeer Mohamed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781479965946

In this paper, a Genetic algorithm (GA) hased supporting vector machine classifier (GA-SVM) is proposed for lymph diseases diagnosis. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to six features using GA. In the second stage, a support vector machine with different kernel functions including linear, Quadratic and Gaussian was utilized as a classifier. The Lymphography database was obtained from the University Medical Center, Institute of Oncology, Ljubljana, Yugoslavia. The obtained classification accuracy was very promising with regard to the other classification applications in the literature for this problem. The performance of SVM classifier with each kernel function was evaluated by using performance indices such as accuracy, sensitivity, specificity, area under curve (AUC) or (ROC), Matthews Correlation Coefficient (MCC) and F-Measure. Linear kernel function obtained highest results which verifies the efficiency of GA-linear stategy.

关键词： diseases genetic algorithms medical diagnostic computing patient diagnosis pattern classification support vector machines GA based supporting vector machine classifier GA-SVM GA-linear stategy Gaussian kernel function Institute of Oncology Ljubljana Lymphography database SVM classifier performance University Medical Center Yugoslavia classification accuracy classification applications genetic algorithm linear kernel function lymph disease dataset lymph disease diagnosis approach performance indices quadratic kernel function algorithm design and analysis Classification algorithms Extraterrestrial measurements Indexes Kernel Prediction algorithms Support vector machines Support Vector Network KERNEL FUNCTIONS Genetic algorithm (GA) medical diagnostic computing Extraterrestrial measurements algorithm design and analysis Academic Medical Centers performance indicators patient diagnosis Prediction algorithms sorting algorithm Pattern recognition Disease diagnosis Kernel maladjustment

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kNN estimation of the unilateral dependency measure between random variables 5

kNN estimation of the unilateral dependency measure between ...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Cataron, Angel Andonie, Razvan Chueh, Yvonne Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Comp Sci Ellensburg WA USA Cent Washington Univ Dept Math Ellensburg WA USA

ISBN: (纸本)9781479945184

The informational energy (IE) can be interpreted as a measure of average certainty. In previous work, we have introduced a non-parametric asymptotically unbiased and consistent estimator of the IE. Our method was based on the kth nearest neighbor (kNN) method, and it can be applied to both continuous and discrete spaces, meaning that we can use it both in classification and regression algorithms. Based on the IE, we have introduced a unilateral dependency measure between random variables. In the present paper, we show how to estimate this unilateral dependency measure from an available sample set of discrete or continuous variables, using the kNN and the naive histogram estimators. We experimentally compare the two estimators. Then, in a real-world application, we apply the kNN and the histogram estimators to approximate the unilateral dependency between random variables which describe the temperatures of sensors placed in a refrigerating room.

关键词： pattern classification regression analysis IE classification algorithms informational energy kth nearest neighbor kNN estimation kNN method nai?ve histogram estimators random variables regression algorithms unilateral dependency Approximation methods Estimation Histograms Ice Probability density function Random variables Temperature sensors Random variable histograms Temperature Sensor Device Component Approximation method Pattern recognition Probability density function Ice Nearest neighbor Joint Research Centre regression analysis sorting algorithm Unilateral

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Determining the impact of demographic features in predicting student success in Croatia

Determining the impact of demographic features in predicting...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Trstenjak, Bruno Donko, Dzenana Medimurje Univ Appl Sci Cakovec Dept Comp Engn Cakovec Croatia Fac Elect Engn Dept Comp Sci Sarajevo Bosnia & Herceg

ISBN: (纸本)9789532330816

Predicting the success of students is a topic which has been studied for a long time in different scientific fields. Evaluation of importance of the features used in the prediction and their subsequent selection is an immensely important step in the process of classification and data mining. This paper presents a study on the importance of student demographic features in the process of predicting. The study and performed analyses used the demographic data collected from the Information System for Higher Education (ISVU). For determining the importance of demographic features in the study the following methods have been used: Information Gain (IG), Gain Ratio (GR), Sequential Backward Selection (SBS), Sequential Forward Selection (SFS). The results show the features rank, their importance weight in the prediction and comparison of the results and the use of different methods. Two classification algorithms for evaluating the impact of ranking features to the quality of prediction are used: Naive Bayes i Support Vector Machine (SVM). Final results provide guidelines for the development of a new prediction model.

关键词： Accuracy Classification algorithms Educational institutions Machine learning algorithms Prediction algorithms Support vector machines sorting algorithm Machine learning algorithms Support Vector Network Prediction algorithms Educational institutions Students Croatia Computer based information systems

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Evaluations of a multiple SOMs method for estimating missing values 7

Evaluations of a multiple SOMs method for estimating missing...

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IEEE/SICE International Symposium on System Integration (SII)

作者： Arima, Kouki Okada, Nobuhiro Tsuji, Yasutaka Kiguchi, Kazuo Kyushu Univ Grad Sch Engn Fukuoka 812 Japan Univ Kitakyushu Fac Environm Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781479969449

Data mining, which is a technique to extract variable information from enormous data, becomes more and more important. Real data often has missing values. Therefore, a method for estimating the missing data is required in application of data mining. Using multiple self-organizing maps (MSOM) proposed by Kikuchi et al. is one of such estimating method. This method does not need a concrete mathematical model and is also available for nonlinear data. However the performance for various missing patterns were unclear, in addition, the comparisons with conventional imputation methods were not provided. This paper demonstrates the performance and the comparison results through simulation experiments with various missing patterns and conventional methods.

关键词： data mining self-organising feature maps MSOM concrete mathematical model imputation methods missing values estimation multiple SOM method multiple self-organizing maps nonlinear data Accuracy Classification algorithms Data mining Data models Educational institutions Neurons Vectors data mining self-organising feature maps sorting algorithm data models Educational institutions Mathematical Model missing value Neurons

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Age Estimation from Face Images: Challenging Problem for Audience Measurement Systems 16

Age Estimation from Face Images: Challenging Problem for Aud...

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16th FRUCT Conference of Open-Innovations-Association

作者： Khryashchev, Vladimir Ganin, Alexander Stepanova, Olga Lebedev, Anton Yaroslavl State Univ Yaroslavl Russia

ISBN: (纸本)9785757704890

The real-time audience measurement system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and in-cloud data statistics analysis. The challenging part of such system is age estimation algorithm on the basis of machine learning methods. The face aging process is determined by different factors: genetic, lifestyle, expression and environment. That is why same age people can have quite different rates of facial aging. We propose a novel algorithm consisting of two stages: adaptive feature extraction based on local binary patterns and support vector machine classification. Experimental results on the FG-NET, MORPH and our own database are presented. Human perception ability in age estimation is studied using crowdsourcing which allows a comparison of the ability of machines and humans.

关键词： face recognition feature extraction image classification learning (artificial intelligence) object detection object tracking statistical analysis support vector machines FG-NET database MORPH database adaptive feature extraction age classification stage age estimation audience measurement system crowdsourcing environment factor expression factor face detection stage face images face tracking stage facial aging gender recognition stage genetic factor in-cloud data statistics analysis stage lifestyle factor local binary pattern machine learning method support vector machine classification algorithm design and analysis Classification algorithms Databases Estimation Face Testing Training Accelerated aging Support vector machine classification Feature extraction algorithm design and analysis Face Face sorting algorithm Facial Recognition Genetic factors Object detection Image classification Support Vector Network Database Crowdsource binary pattern

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New approach for feature selection based on rough set and bat algorithm 9

New approach for feature selection based on rough set and ba...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Emary, E. Yamany, Waleed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Fayoum Univ Fac Comp & Informat Al Fayyum Egypt SRGE Cairo Egypt

ISBN: (纸本)9781479965946

This paper presents a new feature selection technique based on rough sets and bat algorithm (BA). BA is attractive for feature selection in that bats will discover best feature combinations as they fly within the feature subset space. Compared with GAs, BA does not need complex operators such as crossover and mutation, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and runtime. A fitness function based on rough-sets is designed as a target for the optimization. The used fitness function incorporates both the classification accuracy and number of selected features and hence balances the classification performance and reduction size. This paper make use of four initialisation strategies for starting the optimization and studies its effect on bat performance. The used initialization reflects forward and backward feature selection and combination of both. Experimentation is carried out using VCI data sets which compares the proposed algorithm with a GA-based and PSO approaches for feature reduction based on rough-set algorithms. The results on different data sets shows that bat algorithm is efficient for rough set-based feature selection. The used rough-set based fitness function ensures better classification result keeping also minor feature size.

关键词： data reduction feature selection optimisation pattern classification rough set theory BA bat algorithm classification performance fitness function optimization reduction size rough set Accuracy algorithm design and analysis Classification algorithms Equations Indium phosphide Integrated circuits Feature extraction Rough sets algorithm design and analysis Indium phosphide sorting algorithm barium Rough set theory Pattern recognition Bats data reduction bat Chiroptera Dataset

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Classification of Leakage Current Waveforms using Wavelet Packet Transform on High Voltage Insulator 4

Classification of Leakage Current Waveforms using Wavelet Pa...

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International Conference on High Voltage Engineering and Application (ICHVE)

作者： Chaou, A. K. Mekhaldi, A. Moula, B. Teguar, M. Ecole Natl Polytech Alger Lab Rech Electrotech 10 Ave Hassen BadiBP 182 Algiers 16200 Algeria

ISBN: (纸本)9781479966134

In this paper, the Wavelet Packet Transform (WPT) for Leakage Current (LC) examination on high voltage insulators under pollution conditions is exposed. Based on laboratory experiments under various artificial solution natures (consisting in a mixture of distilled water with NaCl, Kaolin or Kieselguhr), LC acquisition is firstly carried out. After a careful examination, three groups of LC waveforms are constituted depending on their peak values. Then, WPT is used to decompose LC waveforms. From this decomposition, feature extraction by energy calculation is processed. Hence, a feature vector, composed of wavelet coefficients energies values, is used as input for three classification algorithms consisting in K-Nearest Neighbors, Naive Bayes and Support Vector Machine, to distinguish between three LC groups. Indeed, this paper introduces WPT for LC investigation and classification.

关键词： Bayes methods feature extraction insulators leakage currents power engineering computing support vector machines wavelet transforms K-nearest neighbor algorithm LC acquisition LC examination Naive Bayes algorithm WPT energy calculation feature vector high voltage insulator leakage current waveform classification pollution condition support vector machine wavelet packet transform Classification algorithms Discrete wavelet transforms Feature extraction Insulators Pollution Wavelet packets Feature extraction Wavelet packets insulators Support Vector Network sorting algorithm K-nearest neighbor algorithm High Voltage Eigenvector discrete wavelet transform Power engineering computing Bayes methods wavelet transforms leakage currents line controller

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Shot Classification for Association Football from TV Broadcasting

Shot Classification for Association Football from TV Broadca...

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IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Siles, Francisco Univ Costa Rica Fac Engn Sch Elect Engn PRIS LAB Pattern Recognit & Intelligent Syst Lab San Jose Costa Rica

ISBN: (纸本)9781479934423

The paper presents the shot classification algorithm used to classify the scenes from football TV broadcasting, to extract only the far view scenes. The far-view scenes are important for the segmentation and tracking of the players during the game, required for devising an abstract model for the interpretation of the game. The system for developing such a platform is named ACE-Football, and is currently being developed at the PRIS-LAB. Results tested using whole games of the latest FIFA's World Cup (FWC) are presented, and the classification algorithm validated using standard metrics, such as precision, recall and F1-score.

关键词： television broadcasting Footballs sorting algorithm Game theory shot Game animals Players Hunting

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Comparing classification models in the final exam performance prediction

Comparing classification models in the final exam performanc...

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37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Gamulin, Jasna Gamulin, Ozren Kermek, Dragutin Univ Zagreb Sch Med Salata 3 Zagreb 41001 Croatia Univ Zagreb Sch Med Dept Phys & Biophys Salata Zagreb Croatia Univ Zagreb Fac Org & Informat Dept Comp & Technol Dept Theoret & Appl Foundat Informat Sci Varazhdin Croatia

ISBN: (纸本)9789532330816

The use of Learning Management Systems (LMS) and the web-based formative and summative assessments during the traditional teaching in classroom provides the huge amount of data on students' behavior and results at the point of time when the course is still in progress. This data could be used for the final exam performance prediction so that the excellent as well as the students requiring help could be detected. The data on 302 students enrolled into the first year Physics course of a biomedical university study program in 2011/2012 have been used. The data were preprocessed by dividing Croatian grading system which comprises 5 grades (range 1-5;1=fail, 5=excellent) into 2 categorical classes in one version of the experiment and into 3 categorical classes in the second version. Several up to date algorithms for classification were applied without and with attributes optimization by a genetic algorithm. Also, since students have 4 chances at 5 exam terms during an academic year, 3 different dependent variables were constructed. In order to evaluate the performance that each of the classification models had the following performance criteria were used: accuracy for all models and sensitivity and area under curve (AUC) for binary classifier systems.

关键词： Accuracy Classification algorithms Data models Education Genetic algorithms Predictive models Support vector machines sorting algorithm data models Genetic algorithm (GA) Prediction models Support Vector Network Education performance prediction Students Performance metrics

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Detection of Aortic Valve Dynamics in Bridge-to-Recovery Feedback Control of the Left Ventricular Assist Device 13

Detection of Aortic Valve Dynamics in Bridge-to-Recovery Fee...

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13th European Control Conference (ECC)

作者： Wang, Yu Faragallah, George Simaan, Marwan A. Univ Louisville Dept Bioengn Cardiovasc Innovat Inst Louisville KY 40202 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9783952426913

Aortic valve dynamics - which implies continuous opening and closing of the aortic valve in each cardiac cycle during the feedback control of the rotary Left Ventricular Assist Devices (LVAD) support - has important clinical implications for patients with mild congestive heart failure. When the LVAD is implanted in such patients as a bridge-to-recovery device, permanent closure of the aortic valve must be avoided by maintaining proper control on the power delivered to the device. In this paper, a new aortic valve dynamics detection algorithm based on a Lagrangian Support Vector Machine (LSVM) model is presented. A detection indicator is derived from the systemic vascular flow signal in the circulatory system using a nonlinear mathematical model of the combined cardiovascular-LVAD system and forms the input to the LSVM classifier. The LSVM classifier is trained and tested to classify the aortic valve dynamics into two states: aortic valve opening and closing (i.e. operating normally) and aortic valve permanently closed. Our results show that the proposed algorithm can detect the aortic valve dynamics effectively in terms of classification accuracy and stability. This classifier will be an integral part in the development of a feedback controller for the LVAD when used on patients as a bridge-to-recovery device. The output of the classifier will be used to adjust the power delivered to the LVAD to ensure that the aortic valve opens and closes normally within each cardiac cycle while at the same time making sure that the physiological demands of the patient are met.

关键词： Adaptive control Classification algorithms Heart Heuristic algorithms Mathematical model Support vector machines Valves Aortic Valve Left Ventricular Assist Device sorting algorithm Heuristic algorithms Support Vector Network Mathematical Model VALVE GATE adaptive control Heart feedback control Classifiers valve opening feedback controller

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