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2013 12th International Conference on Machine Learning and Applications最新文献

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Constrained Motion Particle Swarm Optimization and Support Vector Regression for Non-linear Time Series Regression and Prediction Applications 约束运动粒子群优化与支持向量回归在非线性时间序列回归与预测中的应用
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.164
N. Sapankevych, R. Sankar
{"title":"Constrained Motion Particle Swarm Optimization and Support Vector Regression for Non-linear Time Series Regression and Prediction Applications","authors":"N. Sapankevych, R. Sankar","doi":"10.1109/ICMLA.2013.164","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.164","url":null,"abstract":"Support Vector Regression (SVR) has been applied to many non-linear time series prediction applications [1]. There are many challenges associated with the use of SVR for non-linear time series prediction, including the selection of free parameters associated with SVR training. To optimize SVR free parameters, many different approaches have been investigated, including Particle Swarm Optimization (PSO). This paper proposes a new approach, termed Constrained Motion Particle Swarm Optimization (CMPSO), which selects SVR free parameters and solves the SVR quadratic programming (QP) problem simultaneously. To benchmark the performance of CMPSO, Mackey-Glass non-linear time series data is used for validation. Results show CMPSO performance is consistent with other time series prediction methodologies, and in some cases superior.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127353084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Dual Tree Complex Wavelet Transform Based Multiclass Object Classification 基于对偶树复小波变换的多类目标分类
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.167
A. Khare, M. Khare, R. Srivastava
{"title":"Dual Tree Complex Wavelet Transform Based Multiclass Object Classification","authors":"A. Khare, M. Khare, R. Srivastava","doi":"10.1109/ICMLA.2013.167","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.167","url":null,"abstract":"Multiclass object classification is a difficult problem in computer vision application, because of highly variable nature of different objects. The primary goal of this paper is to classify object into one of the chosen classes. The proposed method uses Dual tree complex wavelet transform coefficients as a feature of object. Dual tree complex wavelet transform is having advantage of its better edge representation and approximate shift-invariant property as compared to real valued wavelet transform. We have used multiclass support vector machine classifier for classification of objects. The proposed method has been tested on dataset prepared by authors of this paper. We have tested the proposed method on multiple levels of Dual tree complex wavelet transform. Quantitative evaluation results demonstrate that the proposed method gives better performance for multiclass object classification in comparison to other state-of-the-art methods.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130050154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
DECOBA: Utilizing Developers Communities in Bug Assignment DECOBA:在Bug分配中利用开发者社区
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.107
Shadi Banitaan, Mamdouh Alenezi
{"title":"DECOBA: Utilizing Developers Communities in Bug Assignment","authors":"Shadi Banitaan, Mamdouh Alenezi","doi":"10.1109/ICMLA.2013.107","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.107","url":null,"abstract":"Bug Tracking System (BTS) is public ally accessible which enables geographically distributed developers to follow the work of each other and contribute in bug fixing. Developer interactions through commenting on bug reports generate a developer social network that can be used to improve software development and maintenance activities. In large scale complex software projects, software maintenance requires larger groups to participate in its activities. Most previous bug assignments approaches assign only one developer to new bugs. However, bug fixing is a collaborative effort between several developers (i.e., many developers contribute their experience in fixing a bug report). In this work, we build developers social networks based on developers comments on bug reports and detect developers communities. We also assign a relevant community to each newly committed bug report. Moreover, we rank developers in each community based on their experience. An experimental evaluation is conducted on three open source projects namely Net Beans, Free desktop, and Mandriva. The results show that the detected communities are significantly connected with high density. They also show that the proposed approach achieves feasible accuracy of bug assignment.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130622984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
WMCD: A Situation Aware Multicast Congestion Detection Scheme Using Support Vector Machines in MANETs 基于支持向量机的动态多播拥塞检测方案
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.45
Xiaoming Liu, H. Nyongesa, James Connan
{"title":"WMCD: A Situation Aware Multicast Congestion Detection Scheme Using Support Vector Machines in MANETs","authors":"Xiaoming Liu, H. Nyongesa, James Connan","doi":"10.1109/ICMLA.2013.45","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.45","url":null,"abstract":"Congestion is one of the most important issues impeding the development and deployment of IP multicast and multicast application in Mobile ad-hoc network (MANETs). In this paper, we propose a situation aware multicast congestion detection scheme with support vector machines in MANETs. We focus on using support vector machines to detect incipient multicast congestion by using structural situation information. In this way, by using a situation aware learning system, we can detect incipient congestion in advance instead of waiting packet loss. The rate adaptation algorithm can reduce the transmission rate only if the loss is classified as a congestion loss. Simulation results show that a support vector machine is an appropriate mechanism for decision making in proactive multicast congestion detection.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130705962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
DroidMLN: A Markov Logic Network Approach to Detect Android Malware DroidMLN:一种检测Android恶意软件的马尔科夫逻辑网络方法
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.184
Mahmuda Rahman
{"title":"DroidMLN: A Markov Logic Network Approach to Detect Android Malware","authors":"Mahmuda Rahman","doi":"10.1109/ICMLA.2013.184","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.184","url":null,"abstract":"Traditional data mining mechanisms with their robustly defined classification techniques have certain limitations to express to what extent the class labels of the test data hold. This problem leads to the fact that a false positive or false negative data point has no quantitative value to express to what degree it is false/true. This situation becomes much severe when it comes to the problem of Malware detection for a growing business market like Android applications. To address the need for a more fine grained model to measure the fitness of the classification we used Markov Logic Network for the first time to detect Android Malwares.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130960949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Preprocessing in Fuzzy Time Series to Improve the Forecasting Accuracy 模糊时间序列预处理提高预测精度
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.185
F. J. J. Santos, H. Camargo
{"title":"Preprocessing in Fuzzy Time Series to Improve the Forecasting Accuracy","authors":"F. J. J. Santos, H. Camargo","doi":"10.1109/ICMLA.2013.185","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.185","url":null,"abstract":"The preprocessing in fuzzy time series has an important role to improve the forecast accuracy. The definitions of domain, number of linguistic terms and of the membership function to each fuzzy set, has direct influence in the forecast results. Thus, this paper has the focus on definition of these parameters, before of performing the prediction. The experimental results in enrollments time series show that, when the forecast is performed after proposed preprocessing, the accuracy rate is improved.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123111076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
A Neural Network Approach to Multi-step-ahead, Short-Term Wind Speed Forecasting 多步超前短期风速预报的神经网络方法
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.130
J. Cardenas-Barrera, J. Meng, Eduardo Castillo Guerra, Liuchen Chang
{"title":"A Neural Network Approach to Multi-step-ahead, Short-Term Wind Speed Forecasting","authors":"J. Cardenas-Barrera, J. Meng, Eduardo Castillo Guerra, Liuchen Chang","doi":"10.1109/ICMLA.2013.130","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.130","url":null,"abstract":"This paper presents a novel neural network-based approach to short-term, multi-step-ahead wind speed forecasting. The methodology combines predictions from a set of feed forward neural networks whose inputs comprehend a set of 11 explanatory variables related to past averages of wind speed, direction, temperature and time of the day, and their outputs represent estimates of specific wind speed averages. Forecast horizons range from 30 minutes up to 6:30 hours ahead with 30 minutes time steps. Final forecasts at specific horizons are combinations of corresponding neural network predictions. Data used in the experiments are telemetric measurements of weather variables from five wind farms in eastern Canada, covering the period from November 2011 to April 2013. Results show that the methodology is effective and outperforms established reference models particularly at longer horizons. The method performed consistently across sites leading up to more than 60% improvement over persistence and 50 % over a more realistic MA-based reference.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116126589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Operation Planning of Hydroelectric Systems: Application of Genetic Algorithms and Differential Evolution 水电系统运行规划:遗传算法与差分进化的应用
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.128
Priscila C. Berbert, A. Yamakami, F. O. França
{"title":"Operation Planning of Hydroelectric Systems: Application of Genetic Algorithms and Differential Evolution","authors":"Priscila C. Berbert, A. Yamakami, F. O. França","doi":"10.1109/ICMLA.2013.128","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.128","url":null,"abstract":"The Operation Planning of Hydroelectric Systems is a large, dynamic, stochastic, interconnected and nonlinear optimization problem. In this model, the minimization of penalized thermal complementation is considered as the objective function with the water discharge of hydroelectric plants at each period as the decision variables. An adaptation of two Evolutionary Metaheuristics, the Genetic Algorithm and the Differential Evolution, are proposed in this paper to solve this problem. These methods consider a set of solutions in order to perform exploration and exploitation of the search space allowing them to find several good quality solutions that can serve as alternatives to a given scenario. Tests performed with the Brazilian Subsystems and compared to one of the current used approaches show that the evolutionary methods can improve current solutions and can also bring the benefit of alternative solutions.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116752654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mining Biomedical Ontologies and Data Using RDF Hypergraphs 利用RDF超图挖掘生物医学本体和数据
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.31
Haishan Liu, D. Dou, R. Jin, P. LePendu, N. Shah
{"title":"Mining Biomedical Ontologies and Data Using RDF Hypergraphs","authors":"Haishan Liu, D. Dou, R. Jin, P. LePendu, N. Shah","doi":"10.1109/ICMLA.2013.31","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.31","url":null,"abstract":"As researchers analyze huge amounts of data that are annotated by large biomedical ontologies, one of the major challenges for data mining and machine learning is to leverage both ontologies and data together in a systematic and scalable way. In this paper, we address two interesting and related problems for mining biomedical ontologies and data: i) how to discover semantic associations with the help of formal ontologies, ii) how to identify potential errors in the ontologies with the help of data. By representing both ontologies and data using RDF hyper graphs, and subsequently transforming the hyper graphs to corresponding bipartite forms, we provide a generalized data mining method that scales beyond what existing ontology-based approaches can provide. We show the proposed method is indeed capable of capturing semantic associations while seamlessly incorporate domain knowledge in ontologies by performing evaluations on real-world electronic health dataset and NCBO ontologies. We also show that our data mining methods can discover and suggest corrections for misinformation in biomedical ontologies.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"13 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 26
A Framework towards the Unification of Ensemble Classification Methods 集成分类方法统一的框架
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.147
Mohammad Ali Bagheri, Q. Gao, Sergio Escalera
{"title":"A Framework towards the Unification of Ensemble Classification Methods","authors":"Mohammad Ali Bagheri, Q. Gao, Sergio Escalera","doi":"10.1109/ICMLA.2013.147","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.147","url":null,"abstract":"Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of the improved classification accuracy in different applications. A large variety of ensemble methods have been proposed in order to exploit strengths of individual classifiers. In this paper, we present a unifying framework for multiple classifier systems, which unites most classification methods by an ensemble of classifiers. Specifically, we link two research lines in machine learning: multiclass classification based on the class binarization techniques and the strategies of ensemble classification. With the proposed framework, the various ensemble classification strategies will be broadly categorized into four main approaches. Then, we provide a brief survey of ensemble methods based on these main approaches as well as principle techniques proposed to combine them.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131637478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
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