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检索条件"主题词=C4.5 Algorithm"

共 123 条记录，以下是61-70 订阅

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comparison of classification Techniques for predicting the cognitive Skill of Students in Education Environment 5

Comparison of Classification Techniques for predicting the C...

引用

5th IEEE International conference on computational Intelligence and computing Research (IEEE IccIc)

作者： Mayilvaganan, M. Kalpanadevi, D. PSG Coll Arts & Sci Coll Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9781479939756

The proposed methodology involves to compares classification techniques for predicting the cognitive skill of students which can be evaluate by conducting the online test. The paper focuses the comparative performance of c4.5 algorithm, Naive Bayes classifier algorithm which one is well suited accuracy for predicting the skill of expertise by experimenting in Rapid miner.

关键词： Datamining c4.5 algorithm Naive Bayes classification Rapid Miner

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Parameter Tuning for ABc-based Service composition with End-to-End QoS constraints 21

Parameter Tuning for ABC-based Service Composition with End-...

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21st IEEE International conference on Web Services (IcWS)

作者： Liu, Ruilin Wang, Zhongjie Xu, Xiaofei Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China

ISBN: (纸本)9781479950546

QoS-aware service composition problem has been drawn great attentions in recent years. As an NP-hard problem, high time complexity is inevitable if global optimization algorithms (such as integer programming) were adopted. Researchers applied various evolutionary algorithms to decrease the time complexity by looking for near optimum solution. However, each evolutionary algorithm has two or more parameters the value of which is to be assigned by algorithm designers and likely has impacts on the optimization results (primarily time complexity and optimality). Our experiments show that there are some dependencies between the features of service composition problems, the value of the evolutionary algorithm's parameters, and the optimization results. In this paper, we use a popular evolutionary algorithm Artificial Bee colony (ABc) to solve service composition problem and focus on the ABc's parameter turning issue. The objective is to identify the potential dependency to help service composition algorithm designers easily set up the values of ABc parameters to obtain preferable composition solution without many times of tedious attempts. Five features of service composition problem, three ABc parameters and two metrics of the final solution are identified. Based on a large volume of experiment data, ABc parameter tuning for a given service composition problem is conducted using c4.5 algorithm and the dependency between problem features and ABc parameters are established using multiple linear regression method. An experiment on a validation dataset shows the feasibility of our approach.

关键词： QoS-aware service composition Artificial Bee conoly (ABc) algorithm parameter tuning c4.5 algorithm

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Decision Trees on the Foreign Exchange Market 8th

Decision Trees on the Foreign Exchange Market

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8th KES International conference on Intelligent Decision Technologies (KES-IDT)

作者： Przemyslaw, Juszczuk Jan, Kozak Katarzyna, Trynda Univ Silesia Inst Comp Sci Sosnowiec Poland Univ Econom Fac Informat & Commun Chair Knowledge Engn Katowice Poland

ISBN: (纸本)9783319396279;9783319396262

In this article we present a novel approach to generate a data set directly from real-world forex market data. The data are transformed into a decision table. Every single object in such a table consists of conditional attributes-in this case values of technical analysis indicators as well as of the decision class (BUY, SELL or WAIT). Our second goal was to test the quality of the classification based on two well-known algorithms used for decision tree construction: the cART algorithm and the c4.5 algorithm. All experiments were conducted on three different currency pairs-with 3 data sets for each pair.

关键词： Forex market Decision tree cART c4.5 algorithm

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A METHOD FOR LANDSLIDE SUScEPTIBILITY ASSESSMENT INTEGRATING ROUGH SET AND DEcISION TREE: A cASE STUDY IN BEIcHUAN, cHINA 36

A METHOD FOR LANDSLIDE SUSCEPTIBILITY ASSESSMENT INTEGRATING...

引用

36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wen, Zhi He, Binbin Xu, Dasong Feng, Qian Univ Elect Sci & Technol China Sch Resources & Environm Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509033324

Landslide susceptibility assessment is necessary for disaster management and safe planning in mountainous regions. In this paper, we have used rough set theory to select ten landslide-related factors (slope, aspect, elevation and so on) derived from maps of geology and remote sensing data, and produced landslide susceptibility map in Beichuan country, Sichuan Province of china based on a model integrating c4.5 decision tree and m-branch smoothing. The performance of method was evaluated by receiver operation characteristic (ROc) curve and the area under the curve (AUc). The result shows that the above initial controlling factors are very important for landslide activity in study area. When the parameter value of m-branch smoothing was set as 14, the method can get the best prediction accuracy with AUc value of 87.16%. These results demonstrated the proposed method for landslide susceptibility assessment is quite effective and can be applied to more disaster assessment problems.

关键词： Landslide susceptibility assessment Rough set c4.5 algorithm Beichuan GIS

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comparison of Attribute Selection Techniques and algorithms in classifying Bad Behaviors of Vocational Education Students

Comparison of Attribute Selection Techniques and Algorithms ...

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2nd IEEE International conference on Digital Ecosystems and Technologies

作者： Wongpun, Sukontip Srivihok, Anongnart Kasetsart Univ Fac Sci Dept Comp Sci Bangkok Thailand

ISBN: (纸本)9781424414895

This study presents the comparison of attribute selection techniques which used for classifying the bad behaviors of vocational education students. There are two classification methods: hybrid classification and single classification. Hybrid classification includes two steps, step one is attribute selection by search method using genetic search and results are compared by three evaluators: 1) correlation-based Feature Selection (cFS) 2) consistency-based Subset Evaluation and 3) Wrapper Subset Evaluation. Step two is the classification of data set by using selected attributed from step one and four classification algorithms. Next, Simple classification used classification algorithms only without attribute selection. The four classification algorithms that used in this experiment for comparing in two methods are : 1) Naive Bayes classifier 2) Baysian Belief Network 3) c4.5 algorithm and 4) RIPPER algorithm. The measurements of classification efficiency had been obtained by using the k-fold cross Validation technique. From the experiment, it was found that hybrid classification technique using genetic search and cFS evaluator with c4.5 algorithm, gives the highest accuracy rate at 82.52%. However, results from F-measure evaluation showed that c4.5 algorithm did not fit for all data types. The hybrid classification technique using genetic search and wrapper subset with Baysian belief network can give a better precision value which can be seen in the F-measure, and it gives the accuracy rate at 82.42%.

关键词： Attribute selection Naive Bayes classifier Baysian belief network c4.5 algorithm RIPPER algorithm

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Sea Wave Detection System using Web-Based Decision Tree algorithm 10

Sea Wave Detection System using Web-Based Decision Tree Algo...

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10th Electrical Power, Electronics, communications, controls and Informatics Seminar (EEccIS)

作者： Kusumah, Yulia Irawan, Budhi Setianingsih, casi Telkom Univ Sch Elect Engn Bandung Indonesia

ISBN: (纸本)9781728171098

Indonesia is a country that is prone to earthquakes. This phenomenon has existed since two tectonic plates crossed Indonesian territory. Also, Indonesia is flanked by two vast oceans, the Pacific Ocean and India. Therefore the tsunami posed a severe threat to the coastal areas of the Indonesian archipelago as well. currently, Indonesia already has a sea wave early detection system. The system is called a buoy made by NOAA (National Oceanic and Atmospheric Administration). The device can detect wave heights and convey information when a tsunami occurs and works in real-time. Unfortunately, these tools cost a high price, and the maintenance is quite complicated, and the maintenance costs are relatively expensive. Based on these problems, we need a system that can handle the problem. The system is designed using the Decision Tree c4.5 algorithm to provide predictions for tourists, fishers, and the surrounding community based on IoT parameters. The system test results show that the c4.5 algorithm has accuracy of around 98% to 100%.

关键词： Sea wave Decision Tree c4.5 algorithm Internet of Things

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Automated Evaluation Results Analysis With Data Mining algorithms

Automated Evaluation Results Analysis With Data Mining Algor...

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12th European conference e-Learning (EcEL)

作者： Bouarab-Dahmani, Farida Tahi, Razika Univ Tizi Ouzou FGEI Fac Dept Comp Sci Tizi Ouzou Algeria Univ Boumerdes Dept Econ Lab Elect Ind Enterprises Boumerdes Algeria

ISBN: (纸本)9781909507845

One of the challenges of research on Technology for Human Learning (THL) and the competency based pedagogy is the personalization of learning. This customization begins with the design of educational systems, applying the strategies defined to monitor and assist the learner in his learning by evaluating his knowledge, skills and detecting and analyzing errors. In this respect, formative evaluation is the process used to capture data on the strengths and weaknesses of a learner during a learning period. These data, to be useful, must be objectively analyzed so that they can be used to manage the following sessions. Data mining is a field at the intersection of statistics and information technology (databases, artificial intelligence, learning etc) whose goal is to discover "interesting" knowledge structures from a set of data. Following our research work on evaluation process and THL, applied to e-learning systems [2] [3] [4], we discuss in this paper a learning cycle with feedback loop integrating formative evaluation followed by the application of data mining algorithms after each learning session. We experimented with a set of tests, the exploration of learners' errors, obtained from a self e-learning by doing tool for the algorithmic domain. We particularly present, in this work, the results given by the application of the c4.5 algorithm for classification and the A Priori one for association rules deduction. These two algorithms were executed via the tool Weka after a data preparing process integrating a relational algebra calculation and format adjustment actions to get adequate input data for Weka algorithms. The results given by these experiments have proved interesting knowledge to complete the learner's profile and facilitate decisions about further learning / teaching / tutoring particularly in the case of distance education.

关键词： data mining e-learning formative assessment a priori algorithm c4.5 algorithm educational data mining

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Design of Intelligent Traffic Simulation System Based on Decision Tree algorithm 2021

Design of Intelligent Traffic Simulation System Based on Dec...

引用

2nd International conference on Artificial Intelligence and Information Systems (IcAIIS )

作者： chen, Peng Guangdong Mech & Elect Polytech Sch Artificial Intelligence Guangzhou Peoples R China

ISBN: (纸本)9781450390200

In recent years, vehicles have become the main transport tools in our daily life along with the economically rapid development in china. The development speed of the municipal roads cross the country has lagged behind the increasing numbers of vehicles and their drivers. Therefore, traffic jams have become one of the major municipal problems in large cities, followed by traffic safety, environmental pollution, and transport efficiency, et al. This paper is aimed to design the intelligent traffic simulation system from the perspective of software architecture and provide relevant suggestions for people's travel through intelligent algorithms, providing a useful reference for the new generation of intelligent transportation system.

关键词： c4.5 algorithm decision Tree intelligent transportation system

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Detection of the Smart Phone Position on User using Inertial Sensors 24

Detection of the Smart Phone Position on User using Inertial...

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24th Signal Processing and communication Application conference (SIU)

作者： Bayar, Veli Yayan, Ugur Yazici, Ahmet Linoyasyon Muhendislik Ltd Sti ETGB Osmangazi Teknopk Eskisehir Turkey Eskisehir Osmangazi Univ Bilgisayar Muhendisligi Bolumu Eskisehir Turkey

ISBN: (纸本)9781509016792

Smart phones are widely used in indoor positioning and navigation systems. Thus, it is important to know the relative position of the smart phone on the user while calculating position with RSS based systems. The accuracy of position will change within this scope. In this study, an intermediary solution is proposed to find the position of the smart phone on the user. Several algorithms are evaluated for the feature selection process. The outputs of the process are estimated via three different classifiers, namely Naives Bayes, k-Nearest Neighborhood and c4.5 decision tree. k-Nearest Neighborhood algorithm achieves 96% accuracy with the output of correlation Feature Selection.

关键词： correlation feature selection k-nearest neighborhood naive bayes c4.5 algorithm J48 algorithm RSS variations wi-fi indoor positioning

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Performing customer Behavior Analysis using Big Data Analytics 7

Performing Customer Behavior Analysis using Big Data Analyti...

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7th International conference on communication, computing and Virtualization (IcccV)

作者： Khade, Anindita A. SIESGST NERUL Bombay Maharashtra India

Although there are many systems that have implemented customer behavior analytics, it's still an upcoming and unexplored market that has greater potential for better advancements. Big data is one of the most rising technology trends that have the capability for significantly changing the way business organizations use customer behavior to analyze and transform it into valuable insights. Even decision trees can be used efficiently for analyzing data. At the end of this paper, a proposed Map Reduce implementation of well-known statistical classifier, c4.5 decision tree algorithm has been proposed. Apart from this,the system aims to implement customer data visualization using Data Driven Documents (***) which allows us to build well customized graphics.

关键词： Big Data analyti c4.5 algorithm D3.j Data visualization Hadoop MapReduce

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