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International Conference on Machine Learning and Cybernetics

作者： Wan, L Lin, ZC Zhao, RC Northwestern Polytech Univ Dept Comp Sci & Engn Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)0780378652

The existing signature verification systems usually train classifiers for a new user by both his/her genuine and forgery signatures. Obviously, the requirement of forgery signatures is impractical. This paper presents an off-line signature verification system that only requires the genuine signatures of a new user. At the training stage the system learns the mapping between the parameters of classifiers without simple forgeries and those with simple forgeries. In the application stage, a primary classifier is trained for a new user without his/her simple forgeries. The final classifier is obtained by transforming the primary classifier via the mapping learnt in the training stage. Experimental results confirm the effectiveness of the proposed system.

关键词： signature verification simple forgery boosting algorithm the prior model

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AN EFFECTIVE AND EFFICIENT HEART DISEASE PREDICTION MODEL USING DISTRIBUTED HIGH PERFORMANCE LIGHT GBM 5

AN EFFECTIVE AND EFFICIENT HEART DISEASE PREDICTION MODEL US...

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Chowdary, B., V Kedarnath, Jaina Vyshnavi, Rachamalla Lavakush, Valluri Shasihdhar, Chavula Vignan Inst Technol & Sci Dept CSE Hyderabad Telangana India

ISBN: (纸本)9781665426428

The healthcare industries acquire massive portions of facts which encompass some hidden information;it virtually is useful for making powerful options. For giving appropriate results as nicely as making dependable options on records, some modern-day-day facts enhancing strategies are used. In this check, A Reliable and additionally Reliable Heart Disease Prediction Version the usage of Distributed High Performance mild GBM is superior for predicting the risk diploma of coronary heart problem. The device makes use of 14 clinical specs collectively with age, sex, immoderate blood strain, ldl cholesterol, weight troubles and so on for prediction. The model anticipates the possibility of sufferers obtaining coronary heart trouble. It allows big records. E.G. Relationships in amongst clinical elements associated with coronary heart sickness similarly to styles, to be installation. The moderate slope improving technique has been used due to the training device. The received consequences have shown that the designed diagnostic device can efficaciously expect the threat of cardiovascular illness.

关键词： GBM boosting algorithm Heart performance

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Lecture video indexing using boosted margin maximizing neural networks 16

Lecture video indexing using boosted margin maximizing neura...

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16th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Ma, Di Zhang, Xi Ouyang, Xu Agam, Gady IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781538614174

This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame image and produces the similarity between them. A boosting framework is integrated into our proposed network during the training process. Experimental results show that the proposed approach is much more capable of handling occlusion, spatial transformations, and other types of noises when compared with known approaches.

关键词： Convolutional neural networks boosting algorithm Lecture video indexing

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An Enhanced Model for Effective Recognition and Segmentation of SLG in Dynamic Video Sequence Using Boosted Learning algorithm

An Enhanced Model for Effective Recognition and Segmentation...

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IEEE 11th International Conference on Networking, Sensing and Control (ICNSC)

作者： Elakkiya, R. Selvamani, K. Kanimozhi, S. Anna Univ Madras 600025 Tamil Nadu India

ISBN: (纸本)9781479931064

This paper proposes a new approach to solve the problem of real-time vision-based hand gesture recognition with the combination of hand posture and hand gesture analyses. The main objective of this is to divide the recognition problem into two levels according to the hierarchical property of hand gestures. This approach implements the posture detection with a statistical method based on Haar-like features and the dynamic approach for recognizing hand gestures using AdaBoost learning algorithm. With this proposed method, a group of hand postures is detected in dynamic video sequence with high recognition accuracy using boosted learning algorithm.

关键词： boosting algorithm Haar-like Features Hand Gesture Recognition Sign Gestures

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Effective Intrusion Detection System Using Semi-Supervised Learning

Effective Intrusion Detection System Using Semi-Supervised L...

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International Conference on Data Mining and Intelligent Computing (ICDMIC)

作者： Wagh, Sharmila Kishor Kolhe, Satish R. MES Coll Engn Dept Comp Engn Pune Maharashtra India North Maharashtra Univ Sch Comp Sci Jalgaon India

ISBN: (纸本)9781479946747

Network security is a very important aspect of internet enabled systems in the present world scenario. As the internet keeps developing the number of security attacks as well as their severity has shown a significant increase. Due to intricate chain of computers the opportunities for intrusions and attacks have increased. Therefore it is need of the hour to find the best ways possible to protect our systems. Every day new kind of attacks are being faced by industries. Hence intrusion detection system are playing vital role for computer security. The most effective method used to solve problem of IDS is machine learning. Getting labeled data does not only require more time but it is also expensive. Labeled data along with unlabeled data is used in semi-supervised methods. The rising field of semi-supervised learning offers a assured way for complementary research. In this paper, an effective semi-supervised method to reduce false alarm rate and to improve detection rate for IDS is proposed.

关键词： Intrusion Detection System Semi-supervised Learning Machine learning boosting algorithm

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Predictive Analysis & Brief Study of Early-Stage Diabetes Using Multiple Classifier Models 12

Predictive Analysis & Brief Study of Early-Stage Diabetes Us...

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IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Tahsin, Mohammad Sadman Jobayer, Md Antor, Md Borhan Uddin Islam, Maidul Raisa, Fatima Fairuz Shaikat, Md Al Hasan BRAC Univ Dept Comp Sci & Engn Dhaka Bangladesh BRAC Univ Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781665483032

Diabetes Mellitus is one of the world's leading causes of mortality, with a worldwide death toll estimated to be in the millions. It is determined by the concentration of a sugar molecule in the blood, which is produced from glucose. Predicting the likelihood of contracting this illness may now be done using a plethora of methods. Data about diabetic patients must be comprehensive and accurate in order to accurately forecast the onset of the disease. In this paper, we discussed early-stage diabetes prediction using six algorithms. The algorithms are Gradient boosting, ADA boosting, XG boosting, Neural Network, SVM, Random Forest, Stacking Neural Network, Stacking SVM, Stacking Random Forest. We also discussed briefly about the best algorithm among them with detailed accuracy by class and confusion matrix. By this study, we can predict early-stage diabetes disease more accurately.

关键词： Diabetes prediction Random Forest boosting algorithm Stacking algorithm confusion matrix

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A new model based on global hybridization of machine learning techniques for "customer chum prediction" 3

A new model based on global hybridization of machine learnin...

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3rd International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Khamlichi, Fahd Idrissi Zaim, Doha Khalifa, Khalid Devoteam Levallois Perret France

ISBN: (纸本)9781728100036

We present a new model based on a global hybridization of the most popular machine learning methods applied to the challenging problem of customer churning prediction in the telecommunications industry. In the first phase of our experiments, all models were applied and evaluated using cross-validation on a popular, public domain dataset. In the second phase, we describe our model and show the performance improvement. In order to determine the most efficient parameter combinations w e performed various simulations for each method and for a wide range of parameters. Our results demonstrate clear superiority of the proposed model against the popular existing ML models.

关键词： Churn prediction machine learning techniques boosting algorithm hybridization

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An Adaptive Ensemble Model for Brain-Computer Interfaces

An Adaptive Ensemble Model for Brain-Computer Interfaces

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IEEE International Conference on Fuzzy Systems (FUZZ)

作者： Hayashi, Isao Tsuruse, Shinji Kansai Univ Dept Informat Takatsuki Osaka 5691095 Japan Panason Syst Solut Japan Co Ltd Tokyo 1040061 Japan

ISBN: (纸本)9781479900206

Brain-computer interface (BCI) have recently entered the research limelight. In many such systems, external computers and machines are controlled by brain activity signals measured using near-infrared spectroscopy (NIRS) or electroencephalograph (EEG) devices. In this paper, we propose a probabilistic data interpolation-boosting algorithm for BCI, where we adopt three evaluation criterions to decide the class of interpolated data around the misclassified data. By using the interpolated data with classes, the discriminated boundary is shown to control the external machine effectively. We verify our boosting method with numerical examples, and discuss the results.

关键词： Brain-Computer Interface boosting algorithm Probabilistic Data Interpolation

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Profiling Phishing Emails Based on Hyperlink Information

Profiling Phishing Emails Based on Hyperlink Information

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International Conference on Advances in Social Network Analysis and Mining (ASONAM)

作者： Yearwood, John Mammadov, Musa Banerjee, Arunava Univ Ballarat Grad Sch ITMS Ballarat Vic 3353 Australia

ISBN: (纸本)9780769541389

In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with whois (***) information on hyperlinks as profile classes. Further, we generate profiles based on classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.

关键词： AdaBoost boosting algorithm classifier predictions computer crime hyperlink information learning (artificial intelligence) multilabel classification problem pattern classification phishing emails profiling support vector machine support vector machines unsolicited e-mail

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Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior

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Journal of Systems and Information Technology 2017年第1-2期19卷 65-93页

作者： Khodabandehlou, Samira Zivari Rahman, Mahmoud Master of Information Technology Engineering Tehran Iran Allameh Tabataba’i University Tehran Iran

Purpose: This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach: The six stages are as follows: first, collection of customer behavioral data and preparation of the data;second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis;third, selection of training and testing data and reviewing their proportion;fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning;fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables;and sixth, providing appropriate strategies based on the proposed model. Findings: According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models. Research limitations/implications: The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries;therefore, generalizing the results to other business centers should be used with caution. Practical implications: Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Bu

关键词： Bagging algorithm boosting algorithm Churn prediction RFM model Supervised machine learning techniques

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