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User preferences based software defect detection algorithms selection using MCDM

INFORMATION SCIENCES 2012年 191卷 3-13页

作者： Peng, Yi Wang, Guoxun Wang, Honggang Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610054 Peoples R China Univ Massachusetts Dept Elect & Comp Engn Dartmouth MA USA

A variety of classification algorithms for software defect detection have been developed over the years. How to select an appropriate classifier for a given task is an important issue in Data mining and knowledge discovery (DMKD). Many studies have compared different types of classification algorithms and the performances of these algorithms may vary using different performance measures and under different circumstances. Since the algorithm selection task needs to examine several criteria, such as accuracy, computational time, and misclassification rate, it can be modeled as a multiple criteria decision making (MCDM) problem. The goal of this paper is to use a set of MCDM methods to rank classification algorithms, with empirical results based on the software defect detection datasets. Since the preferences of the decision maker (DM) play an important role in algorithm evaluation and selection, this paper involved the DM during the ranking procedure by assigning user weights to the performance measures. Four MCDM methods are examined using 38 classification algorithms and 13 evaluation criteria over 10 public-domain software defect datasets. The results indicate that the boosting of CART and the boosting of C4.5 decision tree are ranked as the most appropriate algorithms for software defect datasets. Though the MCDM methods provide some conflicting results for the selected software defect datasets, they agree on most top-ranked classification algorithms. (C) 2010 Elsevier Inc. All rights reserved.

关键词： algorithm selection classification algorithm Knowledge-driven data mining Multi-criteria decision making (MCDM) Software defect detection

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KNN Normalized Optimization and Platform Tuning Based on Hadoop

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IEEE ACCESS 2022年 10卷 81406-81433页

作者： Ma, Chen Chi, Yuhong Xijing Univ Sch Comp Sci Xian 710123 Peoples R China

Big data has become part of the life for many people. The data about people's life are being continously collected, analysized and applied as our society progresses into the big data era. Behind the scene, the computer server clusters need to process hundres of millions pieces of data every day. It is very important to choose the right big data processing platform and algorithm to deal with different kinds of datasets. Therefore, in order to be fully familiar with the related work of driving big data processing, it is necessary to master the classification algorithm of data. It aims to help us carry out a classification model or operation analysis of classification function by screening and classifying the current data in data mining. In addition, the given data can be mapped to the specified category area, and the development trend of future data can be predicted through classification models. So this kind of algorithm helps to reduce the difficulty of work operation and improve people's work efficiency. This paper optimizes the classical classification algorithm-KNN, and designs a new normalized algorithm called PEWM_G KNN. From the perspective of distance measurement, we use Pearson correlation coefficient to replace the traditional Euclidean Metric, then we further refine the study for attribute values of datasets and introduce the entropy weight method, combined with Pearson's measure to optimize the distance calculation equation. After the K value is fixed, we added Gaussian Function to carry out the selection of classification. In this study, we compared the effects of every step, and tested datasets with different data types and sizes, in order to test the performance of the algorithm under different scenarios. The datasets we used include Iris, Breast Cancer, Dry Bean and HTRU2 (All datasets are from The University of California, Irvine). Finally, we further analyze the performance of different system configuration parameters on the prediction rate and time

关键词： Prediction algorithms Optimization Big Data Data models Predictive models Training data Correlation KNN classification algorithm HDFS Hadoop-YARN ZooKeeper Hadoop-HA Hadoop MapReduce

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SPATIOTEMPORAL PATTERN-RECOGNITION USING HIDDEN MARKOV-MODELS

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1995年第4期31卷 1292-1300页

作者： FIELDING, KH RUCK, DW Air Force Institute of Technology USA

A spatio-temporal method for identifying objects contained in an image sequence is presented The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered classification accuracies of 100% and 99.7% are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques.

关键词： feature extraction hidden Markov models image classification image sequences object detection additive correlated noise classification algorithm classification decisions image corruption image sequence object identification object mo Sorting algorithm image sequences Image classification object recognition Object detection Feature extraction Hidden Markov models

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Toward Energy-Awareness Smart Building: Discover the Fingerprint of Your Electrical Appliances

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第4期14卷 1458-1468页

作者： Ma, Meng Lin, Weilan Zhang, Jingbin Wang, Ping Zhou, Yuchen Liang, Xiaoxing Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Peking Univ Sch Software & Microelect Beijing 102600 Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing 100871 Peoples R China Peking Univ Sch Software & Microelect Beijing 100871 Peoples R China Minist Educ Key Lab High Confidence Software Technol PKU Beijing 100871 Peoples R China IBM Res China Beijing 100094 Peoples R China

Energy efficiency raises significant concerns as it is one of the most promising ways to mitigate climate change. Disaggregation and identification of individual electrical appliances activities are one of the essentials for energy preservation especially for smart buildings. This paper proposes a lightweight electrical appliance activity detection approach for smart building, which leverages a single smart metering device to establish a learning and detection processing for multiple appliances. In this system, data interpolation and transition detection algorithm are proposed to effectively reduce the cost of model training and optimize the detection accuracy. The concept of appliance fingerprint is proposed and a variety of fingerprints, including appliance-based and context-based, are defined to depict fine-grained appliance characteristics. Based on these fingerprints, the paper proposes a multisource fingerprint-weighting KNN (FWKNN) classification algorithm and presents a boosting framework for continuous online learning and detection. A prototype system is implemented and demonstrated in IBM Bluemix PaaS cloud platform. Experimental result and analysis prove that FWKNN outperforms other benchmark methods in detection accuracy.

关键词： Activity learning appliance load monitoring (ALM) classification algorithm fingerprint extraction Internet of Things smart building

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Extreme Learning Machine-Based Deep Model for Human Activity Recognition With Wearable Sensors

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COMPUTING IN SCIENCE & ENGINEERING 2019年第5期21卷 16-25页

作者： Niu, Xiaopeng Wang, Zhiliang Pan, Zhigeng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Hangzhou Normal Univ Digital Media & HCI Res Ctr Hangzhou Zhejiang Peoples R China Foshan Univ Foshan Peoples R China

Human activity recognition (HAR) is a main research field of context-aware computing;the performance of HAR mainly depends on the feature extraction method and classification algorithm. Extreme learning machine (ELM) is a single hidden layer neural network, which has better classification and generalization ability. However, ELM is not suitable for feature extraction. Deep learning is a hot research field as it can automatically extract significant features from raw data. In this paper, we propose an approach: an ELM-based deep model, which combined convolutional neural network (CNN), multilayer ELM (ML-ELM) as feature extractor, and used kernel ELM (KELM) as classifier. We used CNN and ML-ELM to extract significant features, and used KELM to achieve stable performance. The performance of proposed approach is validated on two public HAR datasets, and the experimental results show that the proposed approach is effective.

关键词： Convolutional Neural Nets Feature Extraction Learning Artificial Intelligence Pattern classification Sensor Fusion Ubiquitous Computing Feature Extraction Method Single Hidden Layer Neural Network Deep Learning ELM Based Deep Model Convolutional Neural Network CNN Multilayer ELM ML ELM Feature Extractor Kernel ELM Extreme Learning Machine Based Deep Model Human Activity Recognition Wearable Sensors Context Aware Computing classification algorithm Feature Extraction Machine Learning Data Mining Wearable Sensors Brain Modeling Computational Modeling Deep Learning Wearable Computing Pervasive Computing Computing Methodologies Artificial Intelligence Miscellaneous Wearable Ai

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An examination of thematic research,development,and trends in remote sensing applied to conservation agriculture

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International Soil and Water Conservation Research 2024年第1期12卷 77-95页

作者： Zobaer Ahmed Aaron Shew Lawton Nalley Michael Popp V.Steven Green Kristofor Brye Center for Advanced Spatial Technologies University of ArkansasFayettevilleARUSA Environmental Dynamics Program University of ArkansasFayettevilleARUSA Department of Agricultural Economics and Agribusiness University of ArkansasFayettevilleARUSA College of Agriculture Arkansas State UniversityJonesboroARUSA University of Arkansas System Division of Agriculture Little RockARUSA Department of Crop Soiland Environmental SciencesUniversity of ArkansasFayettevilleARUSA

Conservation agriculture seeks to reduce environmental degradation through sustainable management of agricultural *** the 1990s,agricultural research has been conducted using remote sensing technologies;however,few previous reviews have been conducted focused on different conservation management *** of the previous literature has focused on the application of remote sensing in agriculture without focusing exclusively on conservation practices,with some only providing a narrative review,others using biophysical remote sensing for quantitative estimates of the bio-geo-chemical-physical properties of soils and crops,and few others focused on single agricultural management *** paper used the preferred reporting items for systematic review(PRISMA)methodology to examine the last 30 years of thematic research,development,and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales.A set of predefined key concepts and keywords were applied in three databases:Scopus,Web of Science,and Google Scholar.A total of 188 articles were compiled for initial examination,where 68 articles were selected for final analysis and grouped into cover crops,crop residue,crop rotation,mulching,and tillage *** on conservation agriculture research using remote sensing have been increasing since 1991 and peaked at 10 publications in *** the 68 articles,94%used a pixel-based,while only 6%used an object-based classification *** to 2005,tillage practices were abundantly studied,then crop residue was a focused theme between 2004 and *** 2012 to 2020,the focus shifted again to cover *** spectral indices were used in 76%of the 68 *** examination offered a summary of the new potential and identifies crucial future research needs and directions that could improve the contribution of remote sensing to the provision of long-term operat

关键词： Remote sensing Conservation agriculture classification algorithm Spatial resolution Satellite Spectral indices PRISMA

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Defining user spectra to classify Ethereum users based on their behavior

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JOURNAL OF BIG DATA 2022年第1期9卷 1-39页

作者： Bonifazi, Gianluca Corradini, Enrico Ursino, Domenico Virgili, Luca Polytech Univ Marche Dept Informat Engn Ancona Italy

Purpose In this paper, we define the concept of user spectrum and adopt it to classify Ethereum users based on their behavior. Design/methodology/approach Given a time period, our approach associates each user with a spectrum showing the trend of some behavioral features obtained from a social network-based representation of Ethereum. Each class of users has its own spectrum, obtained by averaging the spectra of its users. In order to evaluate the similarity between the spectrum of a class and the one of a user, we propose a tailored similarity measure obtained by adapting to this context some general measures provided in the past. Finally, we test our approach on a dataset of Ethereum transactions. Findings We define a social network-based model to represent Ethereum. We also define a spectrum for a user and a class of users (i.e., token contract, exchange, bancor and uniswap), consisting of suitable multivariate time series. Furthermore, we propose an approach to classify new users. The core of this approach is a metric capable of measuring the similarity degree between the spectrum of a user and the one of a class of users. This metric is obtained by adapting the Eros distance (i.e., Extended Frobenius Norm) to this scenario. Originality/value This paper introduces the concept of spectrum of a user and a class of users, which is new for blockchains. Differently from past models, which represented user behavior by means of univariate time series, the user spectrum here proposed exploits multivariate time series. Moreover, this paper shows that the original Eros distance does not return satisfactory results when applied to user and class spectra, and proposes a modified version of it, tailored to the reference scenario, which reaches a very high accuracy. Finally, it adopts spectra and the modified Eros distance to classify Ethereum users based on their past behavior. Currently, no multi-class automatic classification approach tailored to Ethereum exists yet, albei

关键词： Blockchain classification algorithm Ethereum User spectrum Multivariate Time Series Eros distance Extended Frobenius Norm Etherscan

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Developing an approach to evaluate stocks by forecasting effective features with data mining methods

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第3期42卷 1325-1339页

作者： Barak, Sasan Modarres, Mohammad Islamic Azad Univ Young Researcher Club Ardebil Branch Ardebil Iran Sharif Univ Technol Dept Ind Engn Tehran Iran

In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-based clustering;the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction of risk and return. To illustrate the approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002 to 2011. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Stock market Data mining classification algorithm Feature selection Function-based clustering method

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Detection system of dead and sick chickens in large scale farms based on artificial intelligence

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第5期18卷 6117-6135页

作者： Bao, Yiqin Lu, Hongbing Zhao, Qiang Yang, Zhongxue Xu, Wenbin Nanjing XiaoZhuang Univ Coll Informat Engn Nanjing 211171 Peoples R China Nanjing Univ Coll Software Nanjing 210093 Peoples R China Schulich Sch Business Dept Informat Syst Toronto ON 416647 Canada Nanjing Huazhu Ind Intelligent Equipment Co Ltd Nanjing 211175 Peoples R China

With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.

关键词： artificial intelligence three dimensional total variance machine learning classification algorithm sensor networks

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Performance analysis of the water quality index model for predicting water state using machine learning techniques

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 169卷 808-828页

作者： Uddin, Md Galal Nash, Stephen Rahman, Azizur Olbert, Agnieszka I. Univ Galway Sch Engn Galway Ireland Univ Galway Ryan Inst Galway Ireland Univ Galway MaREI Res Ctr Galway Ireland Charles Sturt Univ Sch Comp Math & Engn Wagga Wagga Australia Charles Sturt Univ Gulbali Inst Agr Water & Environm Wagga Wagga Australia Univ Galway Coll Sci & Engn Civil Engn Galway Ireland

Existing water quality index (WQI) models assess water quality using a range of classification schemes. Conse-quently, different methods provide a number of interpretations for the same water properties that contribute to a considerable amount of uncertainty in the correct classification of water quality. The aims of this study were to evaluate the performance of the water quality index (WQI) model in order to classify coastal water quality correctly using a completely new classification scheme. Cork Harbour water quality data was used in this study, which was collected by Ireland's environmental protection agency (EPA). In the present study, four machine -learning classifier algorithms, including support vector machines (SVM), Naive Bayes (NB), random forest (RF), k-nearest neighbour (KNN), and gradient boosting (XGBoost), were utilized to identify the best classifier for predicting water quality classes using widely used seven WQI models, whereas three models are completely new and recently proposed by the authors. The KNN (100% correct and 0% wrong) and XGBoost (99.9% correct and 0.1% wrong) algorithms were outperformed in predicting the water quality accurately for seven WQI models. The model validation results indicate that the XGBoost classifier outperformed, including accuracy (1.0), pre-cision (0.99), sensitivity (0.99), specificity (1.0), and F1 (0.99) score, in order to predict the correct classification of water quality. Moreover, compared to WQI models, higher prediction accuracy, precision, sensitivity, speci-ficity, and F1 score were found for the weighted quadratic mean (WQM) and unweighted root mean square (RMS) WQI models, respectively, for each class. The findings of this study showed that the WQM and RMS models could be effective and reliable for assessing coastal water quality in terms of correct classification. Therefore, this study could be helpful in providing accurate water quality information to researchers, policy -makers, and water researc

关键词： Water quality index Coastal water quality classification Model uncertainty classification algorithm Cork Harbour

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