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Analog circuit fault diagnosis based on Quantum clustering based Multi-valued Quantum Fuzzification Decision Tree (Qc-MQFDT)

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MEASUREMENT 2016年 93卷 421-434页

作者： cui, Yiqian Shi, Junyou Wang, Zili Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Beihang Univ Sci & Technol Key Lab Reliabil & Environm Engn Beijing Peoples R China

Analog circuit fault diagnosis is challenging due to the parametric deviation and the difficulty in signal quantizing. There still lacks effective approaches to provide reliable fault detection and classification results for a comprehensive diagnosis. In this paper, we propose a fault diagnosis methodology based on a new classification model called Quantum clustering based Multi-valued Quantum Fuzzification Decision Tree (Qc-MQFDT). Qc-MQFDT incorporates the adaptive fuzzification method to discretize continuous-valued data. The fuzzification mechanism is devised by incorporating quantum clustering (Qc) as well as the quantum membership function (QMF), where the former has the ability to sense the internal dependencies of data, and the latter uses the number of energy levels to approximate the optimal shape for fuzzy membership functions. The QF-c4.5 algorithm is developed as the decision tree learning algorithm, which employs quantum fuzzy entropy (QFE) to evaluate the information in the target variable space. The proposed method is validated using both simulated data and the real time data for the application studies of two benchmark analog circuits. The classification performances are discussed and the diagnostic capability of the model is verified through the application studies. (c) 2016 Elsevier Ltd. All rights reserved.

关键词： Decision tree Quantum clustering (Qc) Quantum membership function (QMF) c4.5 algorithm Quantum fuzzy entropy (QFE)

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climate patterns of political division units obtained using automatic classification trees

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ATMOSFERA 2016年第4期29卷 359-377页

作者： coria, Sergio R. Gay-Garcia, carlos Villers-Ruiz, Lourdes Guzman-Arenas, Adolfo Sanchez-Meneses, Oscar Avila-Barron, Oswaldo R. Perez-Meza, Monica cruz-Nunez, Xochitl Lorenzo Martinez-Luna, Gilberto Univ Nacl Autonoma Mexico Ctr Ciencias Atmosfera Circuito Invest Cient S-NCiudad Univ Ciudad De Mexico 04510 DF Mexico Univ Sierra Sur Inst Informat Guillermo Rojas Mijangos S-N Esq Av Univ Miahuatlan De Porfirio D 70800 Oaxaca Mexico Inst Politecn Nacl Ctr Invest Computac Av Juan de Dios Batiz Esq Miguel Othon de Mendiza Mexico City 07738 DF Mexico

This article proposes a methodology to discover patterns in observed climatologic data, particularly temperatures and rainfall, in subnational political division units using an automatic classification algorithm (a decision tree produced by the c4.5 algorithm). Thus, the patterns represent classification trees, assuming that: (1) every political division unit contains at least one climatological station, and (2) the recording periods of the stations are relatively similar in duration and in their initial and ending years. A series of classification models are produced by using different subsets from an experimental dataset. This dataset contains information from 3606 climatological stations in Mexico with recording periods whose durations, initial and ending years are diverse. The target (dependent) variable in all these models is the name of the political unit (i.e., the state). The predictors are 36 monthly features per each climatological station: 12 features corresponding to a minimum temperature, 12 to a maximum temperature, and 12 to cumulative rainfall. The altitude feature is also used as one of the predictors, in addition to the other 36;however, it is used only to quantify its additional contribution to the modelling. The results show that classification trees are effective models for describing and representing non-trivial patterns to characterize the political division units based on their monthly temperatures and rainfalls. One of the remarkable findings is that the cumulative rainfall of May is the feature with highest discrimination capability to the characterization task, which is consistent with the theoretical background on Mexican climatology. In addition, classification trees offer higher expressivity to non-experts in machine learning.

关键词： climate patterns political division Mexico climate data mining data science classification algorithms classification trees c4.5 algorithm

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Machine Learning for Activity Recognition from Movement Time Series Data

Machine Learning for Activity Recognition from Movement Time...

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2017 2nd International conference on computational Modeling, Simulation and Applied Mathematics（cMSAM 2017)

作者： Young-Nam Kim Jin-Hee Park Gyu-Jin Jang Hye-Yeon Yu Moon-Hyun Kim College of Software Sungkyunkwan University

Activity recognition is the core technology of intelligent video surveillance systems and focuses on the behavior of a single object or a pair of objects. A common approach to activity recognition is to first extract and track the characteristics of moving objects from an image sequence, with the goal of converting pixel-level data into low-level functions appropriate for activity analysis. And in these methods, it is important to successfully extract good features and analyze the spatio-temporal interaction of the type of object moving from the extracted features. In this paper, we propose a three step activity recognition model that takes into account the spatial elements of the trajectory using time series data of moving pedestrian trajectories. Activity recognition modeling used the c4.5 algorithm and performed recognition experiments on the four behaviors defined in the cAVIAR dataset.

关键词： Activity recognition Trajectory Time series data 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...

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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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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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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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Data classification for Air Quality on Wireless Sensor Network Monitoring System Using Decision Tree algorithm 2

Data Classification for Air Quality on Wireless Sensor Netwo...

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International conference on Science and Technology-computer (IcST)

作者： Sugiarto, Bambang Sustika, Rika Indonesian Inst Sci Res Ctr Informat Jl Cisitu 21-154 D Bandung 40135 Indonesia

ISBN: (纸本)9781509043576

things for knowing the value of air pollution especially in the cities. In our previous research, we built the air quality monitoring system using wireless sensor network (WSN). Each sensor nodes will transmit all of the air quality data to the base station controller (BSc). This data consists of weather condition, temperature, humidity, carbon monoxide (cO) and carbon dioxide (cO2). In this paper, we propose an implementation of the classification algorithm for classifying air quality in BSc node for generating the danger status of the warning system. By using the c4.5 algorithm, the entropy and information gain values of each case were determined in order to construct the decision tree structure and building the rule sets. From the experiment using confusion matrix, we can see that the proposed decision tree algorithm has the capability to classify the air quality data from sensor nodes with the accuracy of 85.71%, the precision of 81.82%, the sensitivity of 60.00%, and the specificity of 92.31%.

关键词： WSN decision tree classification c4.5 algorithm

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cardiovascular Dysautonomias Diagnosis Using crisp and Fuzzy Decision Tree: A comparative Study 10

Cardiovascular Dysautonomias Diagnosis Using Crisp and Fuzzy...

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10th eHealth conference on Predictive Modeling in Healthcare - From Prediction to Prevention

作者： Kadi, Ilham Idri, Ali Mohammed V Univ ENSIAS Software Project Management Res Team Rabat Morocco

ISBN: (纸本)9781614996453;9781614996446

Decision trees (DTs) are one of the most popular techniques for learning classification systems, especially when it comes to learning from discrete examples. In real world, many data occurred in a fuzzy form. Hence a DT must be able to deal with such fuzzy data. In fact, integrating fuzzy logic when dealing with imprecise and uncertain data allows reducing uncertainty and providing the ability to model fine knowledge details. In this paper, a fuzzy decision tree (FDT) algorithm was applied on a dataset extracted from the ANS (Autonomic Nervous System) unit of the Moroccan university hospital Avicenne. This unit is specialized on performing several dynamic tests to diagnose patients with autonomic disorder and suggest them the appropriate treatment. A set of fuzzy classifiers were generated using FID 3.4. The error rates of the generated FDTs were calculated to measure their performances. Moreover, a comparison between the error rates obtained using crisp and FDTs was carried out and has proved that the results of FDTs were better than those obtained using crisp DTs.

关键词： cardiovascular dysautonomias autonomic nervous system fuzzy logic fuzzy decision tree c4.5 algorithm

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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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Evaluating a decision making system for cardiovascular dysautonomias diagnosis

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SPRINGERPLUS 2016年第1期5卷 1页

作者： Idri, Ali Kadi, Ilham Mohammed V Univ Rabat ENSIAS Software Project Management Res Team Rabat Morocco

Autonomic nervous system (ANS) is the part of the nervous system that is involved in homeostasis of the whole body functions. A malfunction in this system can lead to a cardiovascular dysautonomias. Hence, a set of dynamic tests are adopted in ANS units to diagnose and treat patients with cardiovascular dysautonomias. The purpose of this study is to develop and evaluate a decision tree based cardiovascular dysautonomias prediction system on a dataset collected from the ANS unit of the Moroccan university hospital Avicenne. We collected a dataset of 263 records from the ANS unit of the Avicenne hospital. This dataset was split into three subsets: training set (123 records), test set (55 records) and validation set (85 records). c4.5 decision tree algorithm was used in this study to develop the prediction system. Moreover, Java Enterprise Edition platform was used to implement a prototype of the developed system which was deployed in the Avicenne ANS unit so as to be clinically validated. The performance of the decision tree-based prediction system was evaluated by means of the error rate criterion. The error rates were measured for each classifier and have achieved an average value of 1.46, 2.24 and 0.89 % in training, test, and validation sets respectively. The results obtained were encouraging but further replicated studies are still needed to be performed in order to confirm the findings of this study.

关键词： cardiovascular dysautonomias Data mining c4.5 algorithm Decision tree

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