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The influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. These measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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Performance Evaluation Of Lazy And Decision Tree Classifier: A data Mining Approach for Global Celebrity's Death Analysis 3

Performance Evaluation Of Lazy And Decision Tree Classifier:...

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3rd IEEE international conference on Research in intelligent and Computing in engineering (RICE)

作者： Kumar, Vivek Zinovyev, Roman Verma, Abhishek Tiwari, Prayag Natl Univ Sci & Technol MiSiS Dept Automated Control Syst Moscow Russia Natl Univ Sci & Technol MiSiS Dept Semicond Elect Moscow Russia Malaviya Natl Inst Technol Dept CSE Jaipur Rajasthan India Univ PaPadova Dept Informat Engn Padua Italy

ISBN: (纸本)9781538625996

In present world data is a valuable asset. The best utilization of this asset by means of technology gives an upper hand to an organization. Technologies like machine learning, data mining, and artificial intelligence are no exception to this either. Celebrities influence common people behaviors through biological, psychological and social processes to a great extent. Simultaneously there is vast disparity amongst their demise over the cause, region, and age. Therefore, it is a challenging and interesting endorsement to work upon. The objective of this work is to come up with the comprehensive result to understand the celebrity deaths by investigating the incidence happened over the decade. The database for training is created from the public and open access databases for years 2006-2016 comprising of 11,200 reported deaths over the globe. Findings of the work are year by year extraction of death, the cause behind it, age, gender, and place. Lazy and decision tree classifier model of data mining is being used for the analysis based on the profession as evaluation class.

关键词： machine learning artificial Intelligence data mining celebrity lazy classifier decision tree

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3rd international Afro-European conference for Industrial Advancement, AECIA 2016

3rd International Afro-European Conference for Industrial Ad...

引用

3rd international Afro-European conference for Industrial Advancement, AECIA 2016

ISBN: (纸本)9783319608334

The proceedings contain 37 papers. The special focus in this conference is on Industrial Advancement. The topics include: Integrated autopilot complex system proposal for aircraft model application;maximizing the delivery rate for DTN networks;application to an automated manufacturing system;normalized compression distance based implementation for text data;adaptive methods of process state evaluation: the development of an application for engineering purposes;detection of finger flexions based on decision tree;a new fuzzy clustering algorithm to enhance lifetime of wireless sensor networks;exploratory data analysis of software requirements using statistics and kohonen’s self-organizing map;method for estimation of software requirements using neural network based classification technique;fall detection for elderly based on background subtraction and key points matching;active and reactive power robust control of doubly fed induction generator wind turbine to satisfy new grid codes;scalable addressing mechanism for structured P2P networks;improving the speed and quality of extreme learning machine by conjugate gradient method;new multi-criteria decision-making based on fuzzy similarity, distance and ranking;application in EOLES european project;analysis of air pollution in vertical profile using self-organizing maps;an enhanced delay time model to support evidence based maintenance in healthcare domain;a novel ant colony optimization based cryptanalysis of substitution cipher;software defined stochastic model for moving target defense;adaptive educational games using game metrics and the future of education.

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Design of access control system based on the near field communication (NFC) technology 3

Design of access control system based on the near field comm...

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2018 3rd international conference on Insulating Materials, Material Application and Electrical engineering, IMMAEE 2018

作者： Li, Biqing Tian, Miao Zheng, Shiyong Jiang, Suping School of Information and Communication Engineering Hezhou University Hezhou Guangxi542899 China School of Software Henan University Kaifeng Henan475004 China Wuhan University Wuhan430070 China Guangxi Talent International College Qinzhou Guangxi535000 China

With the development of mobile Internet technology, Android smart phones is gradually integrated into people's lives, and become important information terminal. Short-range wireless communication (NFC) is one of the technologies for use in access control systems, this kind of short-distance wireless communication standard can be achieved within a few centimeters of data exchange between devices. NFC is also in full compliance with ISO standards for management of contactless smart card, which is a notable feature of it an ideal platform. Android smartphones are gradually replace most of the commonly used tools in our lives, such as cameras, maps and so on, and NFC functionality based on the Android platform will gradually replace the life of cash payments and IC card access control system, NFC has become an important trend of development of intelligent access control system. This design from two aspects of hardware and software to take on Android platform-based NFC function plays an important role in access control systems. This design is based on the Android phone's NFC features using single-chip computer system relays, electromagnetic locks, smart home functions, NFC contactless door way humans bring more convenience, farewell forgot to bring my keys at home will not be suffering. © 2018 Published under licence by IOP Publishing Ltd.

关键词： Smartphones

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Smart Underwriting System: An intelligent Decision Support System for Insurance Approval & Risk Assessment

Smart Underwriting System: An Intelligent Decision Support S...

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international conference for Convergence of Technology (I2CT)

作者： Aman Dubey Tejisman Parida Akshay Birajdar Ajay Kumar Prajapati Sagar Rane Computer Engineering Army Institute of Technology Pune

Over the past years, insurance industry poses many challenges, one of them is maintaining the data either in legacy systems or in paper files for underwriting transaction. Most of the insurance companies are automating their data collection process. Traditionally, information of the client (such as personal details, medical records etc.) who needs insurance is sent to the underwriter through an email and after proper analysis, underwriter sends the quickQuote back to the agent based on his intuition and experience. Generally, quickQuote consists of insurance approval conditions and insurance plan name. Due to enormous amount of diseases and medicines, complexity in underwriting process has been increased. In a nutshell, an improved and optimized way of underwriting process is required. Introducing Artificial Intelligence can help to transform the traditional underwriting process to smart one. Usually data given to the underwriter is in unstructured format. Using Natural Language Processing and by training numerous statistical machine learning classifiers over the unstructured texts, important features were extracted out from unstructured emails. Main challenge is to exploit the information embedded in emails using automated tools, because of noisiness, uncleaned and unstructured data. Based on the features extracted, a model was trained and tested for unseen mails to get the proper insurance plan name and advice. This data was drafted to a template and sent back to the agent through an automated email reply. Main Objective of the project is to handle dynamic situations efficiently and to automate the underwriting task.

关键词： Feature extraction Cancer Postal services Insurance Training Electronic mail Support vector machines

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Sparse Representation Based on Discriminant Locality Preserving Dictionary learning for Face Recognition 18th

Sparse Representation Based on Discriminant Locality Preserv...

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Feng, Guang Li, Hengjian Dong, Jiwen Chen, Xi Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Guizhou Normal Univ Sch Bigdata & Comp Sci Guiyang Guizhou Peoples R China

ISBN: (纸本)9783319689357;9783319689340

A novel discriminant locality preserving dictionary learning (DLPDL) algorithm for face recognition is proposed in this paper. In order to achieve better performance and less computation, dimensionality reduction is applied on original image samples. Most of the proposed dictionary learning methods learn features and dictionary, however, the inner structure of feature is hardly considered. Therefore, by incorporating discriminant locality preserving criteria into dictionary learning, the margin of coefficients distance between between-class and within-class is encourage to be large in order to enhance the classification ability and gain discriminative information. What is more, the local structure of the feature is also preserved, which is very vital in face recognition performance. Our experiments on Extended Yale B, AR and CMU face database demonstrated the proposed algorithm has higher recognition performance than other dictionary learning based classification methods.

关键词： Dictionary learning Sparse representation Discriminant locality preserving criterion Face recognition

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Predicting Personality Traits of Users in Social Networks 18th

Predicting Personality Traits of Users in Social Networks

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Ye, Zhili Du, Yang Zhao, Li Chinese Acad Sci Inst Software 4 South Fourth St Beijing 100190 Peoples R China Univ Chinese Acad Sci 80 ZhongGuanCun East Rd Beijing 100190 Peoples R China

ISBN: (纸本)9783319689357;9783319689340

With the popularity of social media, an emerging interest is on predicting personality by mining users' digital footprints. Some researches have proven that social behaviors of users in social network are strongly influenced by their personality. However, current methods are mainly focused on selecting better features from user behaviors and then utilizing a classical classification model to separately predict different personality traits. Based on our observation and statistic analysis from the data, this paper proposes a unified semi-supervised method, Personality-dependent Variable Factor Graph model (PVFG), by considering not only the interrelations between a person's personality traits, but also the interrelations with their friends' personality traits. The experiment is carried on two real-world datasets and the results indicate that the proposed method outperforms several alternative methods.

关键词： Personality prediction Social network Factor graph Multi-label

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Predicting learning Effect by Learner's Behavior in MOOCs 18th

Predicting Learning Effect by Learner's Behavior in MOOCs

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Tian, Ye Wen, Yimin Yi, Xinhe Yang, Xi Miao, Yuqing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur 1 Jinji Rd Guilin 541004 Peoples R China Guangxi Dist Educ Dept 69 Zhuxi Ave Nanning 5530021 Peoples R China

ISBN: (纸本)9783319689357;9783319689340

With the fast development of MOOCs in recent years, more and more people start to take MOOCs to perfect themselves. However, there exist high dropout rate and low passing rate of examination in many courses. So it is very important to predict learners' learning effect exactly. For learners who predicted good learning effect, teachers can impose intervention to help these learners to stick to the end of courses, while for predicted bad learning effect, teachers can take measures to help these learners to study harder to improve their learning. In this paper, we first analyze learners' learning behavior data to explore the differences among learners with different categories, then a cascade prediction model is proposed to predict whether a learner can earn certificate in a course. Experiments conducted on a real-world dataset illustrated the effectiveness of the proposed model.

关键词： MOOCs learning effect learning behavior Prediction model Education data mining

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Wang, Zhongyu Tang, Wang Pi, Dechang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Prediction of remaining useful life (RUL) has widely application in industrial domain, especially for aircraft where safety and reliability are of high importance. RUL Prediction can provide the time of failure for a degrading system, so that there are high requirements of its accuracy. In this paper, we propose a new trajectory similarity-based RUL prediction approach with an information fusion strategy (named IF-TSBP) in the similarity measure step. The novel information fusion strategy allows us to get more precise trajectory similarity degree than traditional similarity measure strategy which contributes to the prediction result. The experimental results show that the prediction accuracy of our proposed algorithm IF-TSBP outperforms the traditional trajectory similarity-based prediction approach and some common machine learning algorithms.

关键词： Remaining useful life Similarity measure Prognostics

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Face Anti-spoofing Algorithm Based on Gray Level Co-occurrence Matrix and Dual Tree Complex Wavelet Transform 18th

Face Anti-spoofing Algorithm Based on Gray Level Co-occurren...

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18th international conference on intelligent data engineering and automated learning (ideal)

作者： Qu, Xiaofeng Li, Hengjian Dong, Jiwen Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China

ISBN: (纸本)9783319689357;9783319689340

By analyzing the difference of facial texture features between living face and photo, we propose a novel face anti-spoofing algorithm based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet tree (DT-CWT). Firstly, inspired by the co-occurrence matrix, we extract five texture features including angle second moment, entropy, contrast, correlation and local uniformity to represent the gray direction, interval and amplitude information for the face texture information. Secondly, DT-CWT has the advantages of approximate translation invariance and good direction selectivity. Therefore, the coefficients of DT-CWT can enhance the texture information and edge information in the frequency domain. At last, the SVM classification is used to distinguish between true and fake face. Our algorithm is demonstrated on the published NUAA database. Compared with the existing methods, the feature dimension is reduced. The experimental results show that the proposed algorithm improves the detection accuracy.

关键词： Anti-spoofing Liveness detection Gray level co-occurrence matrix Dual tree complex wavelet transform

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