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3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Xuan Lei School of Control and computer Engineering North China Electric Power University

The partial classification algorithm is mainly used to predict the popularity of network news and to explore the best model to predict the popularity of network news, so as to help network news service providers predict the popularity of news before publication. The popularity of network news is predicted according to the data analysis process: first, UCI data sets are pre-processed;secondly, feature selection is conducted for the data sets by using recursive feature elimination algorithm;then modelling and analysis is carried out, and finally through the confusion matrix, risk map and ROC(Receiver Operating Characteristic) chart performance evaluation, the performance of the model is compared and analyzed. Through comparison, it is found that random forest is the best prediction model.

关键词： network news feature selection classification algorithm model evaluation

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Embedding Na?ve Bayes algorithm Data Model in Predicting Student Graduation

Embedding Na?ve Bayes Algorithm Data Model in Predicting Stu...

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作者： Ace C.Lagman Joseph Q.Calleja Ma.Corazon G.Fernando Joseph G.Gonzales John Benedict Legaspi John Heland Jasper C.Ortega Ronel F.Ramos Maria Vicky S.Solomo Regina C.Santos FEU Institute of Technology

In the Philippines,according to Philippine Authority of Statistics,there is an imbalance between the student enrollment and student *** half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on *** study aims to utilize how Na?ve Bayes algorithm-a data classification algorithm that is based on probabilistic analysis-can be used in educational data mining specifically in student *** study is focused on the application of the Na?ve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time,so proper remediation and retention policies can be formulated and implemented by institutions.

关键词： Na?ve Bayes machine learning algorithm data mining student graduation prediction Big Data Analytics classification algorithm

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Classify high dimensional datasets using discriminant positive negative association rules 5

Classify high dimensional datasets using discriminant positi...

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5th Asian Conference on Defense Technology (ACDT)

作者： Thanh Do Van Hieu Do Duc Giap Cu Nguyen Nguyen Tat Thanh Univ Dept Informat Technol Hcm Vietnam Univ Sci & Tech Hanoi VAST Dept Infor & Com Technol Hanoi Vietnam Thuongmai Univ Dept Manag Infor Sys & E Com Hanoi Vietnam

ISBN: (纸本)9781538676783

The purpose of this paper is to investigate the ability of binary classification using the possitive negative association rules mining (PNARs) for large dataset. The PNARs (as understood until now) called narrow PNARs (NPNARs for short) as well as their generalized forms called expanded PNARs (EPNARs for short) will be investigated. The paper proposes a classification algorithm based on mining the discriminant NPNARs and EPNARs. The algorithm integrates Dimensionality Reduction, Rule Generation, Removal of redundant rules and classification. The number of classified instances in testing datasets and the classification performance using the valid disriminant NPNARs and EPNARs found out by the proposed algorithm are compared with each other and with 12 other classification algorithms. Experimental results show that the mining of EPNARs rather than that of NPNARs has prospects to he a classification technique applied in the real world. The paper also outlines some issues that need to be researched in the near future so that using the mining of the discriminant EPNARs becomes an efficient technique for classifying high dimensional datasets.

关键词： classification prediction dimensionality reduction positive nagative association rules classification algorithm

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The Advance of Support Tensor Machine 16

The Advance of Support Tensor Machine

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16th IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Xiang, Yi He, Jing Wu, LiWen Jiang, Qian Jin, Xin Yao, Shaowen Yunnan Univ Sch Software Kunming Yunnan Peoples R China

ISBN: (纸本)9781538658864

In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.

关键词： Support Tensor Machine Tensor Decomposition classification algorithm

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First Place Solution for NLPCC 2017 Shared Task Social Media User Modeling 1

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6th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Qian, Lingfei Wang, Anran Wang, Yan Huang, Yuhang Wang, Jian Lin, Hongfei Dalian Univ Technol Dalian 116023 Liaoning Peoples R China

ISBN: (数字)9783319736181

ISBN: (纸本)9783319736181;9783319736174

With the popularity of mobile Internet, many social networking applications provide users with the function to share their personal information. It is of high commercial value to leverage the users' personal information such as tweets, preferences and locations for user profiling. There are two subtasks working in user profiling. Subtask one is to predict the Point-of-Interest (POI) a user will check in at. We adopted a combination of multiple approach results, including user-based collaborative filtering (CF) and social-based CF to predict the locations. Subtask two is to predict the users' gender. We divided the users into two groups, depending on whether the user has posted or not. We treat this task subtask as a classification task. Our results achieved first place in both subtasks.

关键词： Location prediction Gender prediction User modeling Collaborative filtering classification algorithm

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Feature Analysis and Optimization of Underwater Target Radiated Noise Based on t-SNE 10

Feature Analysis and Optimization of Underwater Target Radia...

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10th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Chen, Yuechao Du, Shuanping Quan, Hengheng Hangzhou Appl Acoust Res Inst Sci & Technol Sonar Lab Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538661192

The analysis and optimization for underwater target radiated noise recognition are addressed. The t-SNE (t-distributed stochastic neighbor embedding) algorithm is taken for dimension reduction of the underwater target radiated noise spectrum bands segmented by frequency and the visual results can be obtained. The average distance between classes and coincidence rate are constructed as indicators of the visualization results. By analyzing the separability of each frequency band, the optimal features can be obtained. Then the optimal features are recognized by three classification algorithms which are Random Forest, SVM (Support Vector Machine) and AdaBoost. The processing results of experimental signal spectrum with two types of target are as follows. The separability of the target signal spectrum decreases with the increase of frequency. The spectrum band of 10-150Hz has the best separability. SVM is sensitive to the increase of the data dimension with non optimal separability. Random Forests and AdaBoost can apply separability components in wider frequency bands and have greater tolerance. The overall recognition accuracy of AdaBoost is the highest, but the computation efficiency is the lowest. These analysis results shows that the t-SNE algorithm can be used to optimize the underwater target radiated noise spectrum features for the purpose of improving the accuracy and efficiency of the classification algorithm.

关键词： underwater target radiated noise t-distributed stochastic neighbor embedding classification algorithm feature optimization spectrum

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Feature Selection and Comparison of Machine Learning algorithms in classification of Grazing and Rumination Behaviour in Sheep

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SENSORS 2018年第10期18卷 3532-3532页

作者： Mansbridge, Nicola Mitsch, Jurgen Bollard, Nicola Ellis, Keith Miguel-Pacheco, Giuliana G. Dottorini, Tania Kaler, Jasmeet Univ Nottingham Sch Vet Med & Sci Sutton Bonington Campus Loughborough LE12 5RD Leics England Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England Univ Nottingham Adv Data Anal Ctr Nottingham NG8 1BB England Intel Labs Internet Things Syst Res Leixlip W23 CX68 Ireland

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy;this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

关键词： sheep behaviour grazing rumination behaviour classification algorithm accelerometer and gyroscope sensor machine learning precision livestock monitoring

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Minimum Annotation identification of facial affects for Video Advertisement 2

Minimum Annotation identification of facial affects for Vide...

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2nd International Conference on Intelligent Circuits and Systems (ICICS)

作者： Goyal, Gaurav Singh, Jaiteg Chitkara Univ Dept Comp Sci & Engn Chandigarh Punjab India

ISBN: (纸本)9781538664834

Consumer's market has witnessed a radical change. It has shifted from being a seller's market to buyer's market. Reviews and feedback play an important role deciding the marketing strategy. These reviews express opinions or sentiments of a particular individual for a particular product, which contains information that is useful for others who are interested in buying that product. The paper proposes a model to automate the product review for an advertisement by making use of affective computing. The computerized model for Facial emotion recognition using wearable biometric sensors is used analyze the consumer behavior on a video advertisement. The model detects five basic emotions of happy, sad, surprise, anger and neutral. The logistic regression classification algorithm was used to classify the various sentiments given by various users against the advertisement on the given test data. The result of the classification algorithm has been compared with training data using WEKA. The accuracy of 82% is obtained through classification algorithm.

关键词： Affective computing Neuromarketing classification algorithm Support Vector Machine Galvanic Skin Response

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A Critical Study of classification algorithms Using Diabetes Diagnosis 6

A Critical Study of Classification Algorithms Using Diabetes...

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IEEE 6th International Advance Computing Conference (IACC)

作者： Srikanth, Panigrahi Deverapalli, Dharmaiah VNR VJIET Dept Informat Technol Hyderabad Telagana India Shri Vishnu Engn Coll Woman Dept Informat Technol Bhimavaram India

ISBN: (纸本)9781467382861

This paper predicts the Diabetes Disease based on Data Mining Techniques of classification algorithms. classification algorithm and tools may reduce heavy work on Doctors. In this paper Evaluated as classification algorithms for the Classify of some Diabetes Disease Patient Datasets. Data Mining is one of the main algorithm is classification. classification algorithm Examine of the Decision Tree algorithm, Byes algorithm and Rule based algorithm. These algorithms are evaluate Error Rates and identify of the patients based evolution Function of the measure the accurate results.

关键词： Pima Diabetes Disease Data set classification algorithm Decision Tree algorithm Byes algorithms and Rules

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Superpixel-based class-semantic texton occurrences for natural roadside vegetation segmentation

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MACHINE VISION AND APPLICATIONS 2017年第3-4期28卷 293-311页

作者： Zhang, Ligang Verma, Brijesh Cent Queensland Univ Sch Engn & Technol Brisbane Qld Australia

Vegetation segmentation from roadside data is a field that has received relatively little attention in present studies, but can be of great potentials in a wide range of real-world applications, such as road safety assessment and vegetation condition monitoring. In this paper, we present a novel approach that generates class-semantic color-texture textons and aggregates superpixel-based texton occurrences for vegetation segmentation in natural roadside images. Pixel-level class-semantic textons are learnt by generating two individual sets of bag-of-word visual dictionaries from color and filter bank texture features separately for each object class using manually cropped training data. For a testing image, it is first oversegmented into a set of homogeneous superpixels. The color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric, resulting in a color and a texture texton occurrence matrix. The color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy. Evaluations on two datasets such as video data collected by the Department of Transport and Main Roads, Queensland, Australia, and a public roadside grass dataset show high accuracy of the proposed approach. We also demonstrate the effectiveness of the approach for vegetation segmentation in real-world scenarios.

关键词： Vegetation segmentation Texton Superpixel classification algorithm Object recognition

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