2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)最新文献

Predicted probability enhancement for multi-label text classification using class label pair association 基于类标签对关联的多标签文本分类预测概率增强

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604107

Mohammad Salim Ahmed, Sourabh Jain, F. B. Muhaya, L. Khan

{"title":"Predicted probability enhancement for multi-label text classification using class label pair association","authors":"Mohammad Salim Ahmed, Sourabh Jain, F. B. Muhaya, L. Khan","doi":"10.1109/EAIS.2013.6604107","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604107","url":null,"abstract":"In order to extract knowledge from the growing information available over the Internet, it is imperative that we classify the information first. Classification is a vastly researched topic in the field of data mining and text data, representing a significant portion of the information, naturally has acquired significant research interest. However, text data classification presents its own problems of high and sparse dimensionality, as attributes span over huge set of words of natural language and multi-label property as each document may belong to more than one class simultaneously. Any solution proposed to classify such data without considering these facts cannot render optimum results. In this paper, we have discussed an approach based on fuzzy clustering to handle high dimensionality of data and using inter-class correlation information in the form of class label pairs to enhance the prediction probabilities in multi-label classification as a post processing step. We use correlation information in both positive (rewarding) and negative (penalizing) terms to enhance the probability metrics for multi-label classification. We have tested our proposed algorithm on a number of benchmark data sets and have been able to achieve better performance than the existing approaches.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328592","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

An ensemble based Genetic Programming system to predict English football premier league games 基于集成的遗传规划系统预测英国足球超级联赛

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604116

Tianxiang Cui, Jingpeng Li, J. Woodward, A. Parkes

引用次数: 12

Ensemble method based on individual evolving classifiers 基于个体进化分类器的集成方法

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604105

J. A. Iglesias, Agapito Ledezma, A. Sanchis

引用次数: 12

Online identification of complex multi-input-multi-output system based on generic evolving neuro-fuzzy inference system 基于泛型演化神经模糊推理系统的复杂多输入多输出系统在线辨识

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604112

Mahardhika Pratama, S. Anavatti, M. Garratt, E. Lughofer

{"title":"Online identification of complex multi-input-multi-output system based on generic evolving neuro-fuzzy inference system","authors":"Mahardhika Pratama, S. Anavatti, M. Garratt, E. Lughofer","doi":"10.1109/EAIS.2013.6604112","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604112","url":null,"abstract":"Nowadays, unmanned aerial vehicles (UAV) play a noteworthy role in miscellaneous defence and civilian operation. A major facet in the UAV control system is an identification phase feeding the valid and up-to-date information of the system dynamic in order to generate proper adaptive control action to handle various UAV maneuvers. UAV, however, constitutes a complex system possessing a highly non-linear property. Conversely, the learning environment in modeling UAV's dynamic varies overtime and demands online learning scheme encouraging a fully adaptive and evolving algorithm with a mild computational load to settle the task. In contrast, contemporaneous literatures scrutinizing the identification of UAV dynamic yet rely on offline or batched learning procedures. Evolving neuro-fuzzy system (ENFS) where the landmarks are flexible rule base and usable in the time-critical applications offers a promising impetus in the UAV research field, and in particular its identification standpoint. The principle cornerstone is ENFS can commence its learning mechanism from scratch with an empty rule base and very limited expert knowledge. Nonetheless, it can perform automatic knowledge building from streaming data without catastrophic forgetting previous valid knowledge which is alike autonomous mental development of human brain. This paper elaborates the identification of rotary wing UAV based on our incipient ENFS algorithm termed generic evolving neuro-fuzzy system (GENEFIS). In summary, our algorithm can not only trace footprint of the UAV dynamic but also ameliorate the performance of existing ENFS in terms of predictive quality and resultant rule base burden.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128063388","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: 7

Evolving systems for computer user behavior classification 计算机用户行为分类的进化系统

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604108

J. A. Iglesias, Agapito Ledezma, A. Sanchis

引用次数: 8

The IEEE Computer Society Smart Grid Vision Project opens opportunites for computational intelligence IEEE计算机协会智能电网视觉项目为计算智能提供了机会

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604117

D. Cartes, J. Chow, D. McCaugherty, S. Widergren, G. Venayagamoorthy

{"title":"The IEEE Computer Society Smart Grid Vision Project opens opportunites for computational intelligence","authors":"D. Cartes, J. Chow, D. McCaugherty, S. Widergren, G. Venayagamoorthy","doi":"10.1109/EAIS.2013.6604117","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604117","url":null,"abstract":"The IEEE Computer Society Smart Grid Vision Project (CS-SGVP) was chartered to develop Smart Grid visions looking forward as far as 30 years into the future. At the completion of the project it was realized that to address the complexity of a Smart Grid with vast numbers of intelligent connected devices and systems, computational intelligence techniques must move from top-down to the lowest levels of architectures, with interactive cooperation between smart components, each with a level of autonomy. The CS-SGVP team emphasized creative thought leadership and “blue sky” thinking to identify future Smart Grid operational visions and the role of computing to achieve these visions. The CS-SGVP team developed its visions using a three-tiered approach. Architectural concepts describe Smart Grid goals and characteristics, general grid types, as well as computing concepts considered common across grid types. Functional concepts describe how the grid will operate. Technological concepts describe the roles of certain technologies within the Smart Grid. The CS-SGVP expects that over the course of many years, various visions will come to fruition.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191738","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

A Meta-cognitive Interval Type-2 fuzzy inference system classifier and its projection based learning algorithm 一种元认知区间2型模糊推理系统分类器及其投影学习算法

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604104

K. Subramanian, R. Savitha, S. Sundaram

{"title":"A Meta-cognitive Interval Type-2 fuzzy inference system classifier and its projection based learning algorithm","authors":"K. Subramanian, R. Savitha, S. Sundaram","doi":"10.1109/EAIS.2013.6604104","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604104","url":null,"abstract":"In this paper, we present a Meta-cognitive Interval Type-2 neuro-Fuzzy Inference System (McIT2FIS) classifier and its projection based learning algorithm. McIT2FIS consists of two components, namely, a cognitive component and a meta-cognitive component. The cognitive component is an Interval Type-2 neuro-Fuzzy Inference System (IT2FIS) represented as a six layered adaptive network realizing Takagi-Sugeno-Kang type inference mechanism. IT2FIS begins with zero rules, and rules are added and updated depending on the relative knowledge represented by the sample in comparison to that represented by the cognitive component. The knowledge representation ability of IT2FIS is controlled by a self-regulatory learning mechanism that forms the meta-cognitive component. As each sample is presented to the network, the meta-cognitive component monitors the hinge-loss error and class-specific spherical potential of the current sample to decide what-to-learn, when-to-learn and how-to-learn them, efficiently. When a new rule is added or when an existing rule is updated, a Projection Based Learning (PBL) algorithm uses class specific criterion and sample overlap criterion to estimate the network parameters corresponding to the minimum energy point of the error function. The performance of McIT2FIS is evaluated on a set of benchmark classification problems from UCI machine learning repository. The statistical performance comparison with other algorithms available in the literature indicates improved performance of McIT2FIS.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130613892","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: 28

ARFA: Automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation ARFA:使用进化聚类、分类器和递归密度估计的自动实时飞行数据分析

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604110

Denis Kolev, P. Angelov, Garegin Markarian, M. Suvorov, S. Lysanov

{"title":"ARFA: Automated real-time flight data analysis using evolving clustering, classifiers and recursive density estimation","authors":"Denis Kolev, P. Angelov, Garegin Markarian, M. Suvorov, S. Lysanov","doi":"10.1109/EAIS.2013.6604110","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604110","url":null,"abstract":"In this paper a novel approach to autonomous real time flight data analysis (FDA) is proposed and investigated. The anomaly detection is based on recursive density estimation (RDE) and the fault identification is based on the evolving self-learning classifiers introduced recently. The paper starts with a brief critical analysis of the currently used FDA methods and tools. Then the problems of fault detection (FD) and identification are described formally. The importance of the ability to process the data in real time and on-line (in flight) is directly related to the efficiency and safety. Therefore, in this paper the focus is on the recursive approaches which are computationally lean and suitable for on-line mode of operation. The novel concept of ARFA (Automated Real-time FDA) is then applied to real flight data from Russian and USA made aircrafts. The results are compared and analyzed. Both, advantages that this novel methodology and algorithms offer as well as the current limitations and future directions of research are pointed out and future work outlined.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"224 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128429033","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

Dynamic and evolving fuzzy concept lattices 动态和演化的模糊概念格

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604101

Trevor P. Martin

引用次数: 2

Evolution of a digital organism playing Go 一个下围棋的数字有机体的进化

2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2013-04-16 DOI: 10.1109/EAIS.2013.6604115

C. Alt, H. A. Mayer

引用次数: 0