Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604107
Mohammad Salim Ahmed, Sourabh Jain, F. B. Muhaya, L. Khan
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.
{"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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604116
Tianxiang Cui, Jingpeng Li, J. Woodward, A. Parkes
Predicting the result of a football game is challenging due to the complexity and uncertainties of many possible influencing factors involved. Genetic Programming (GP) has been shown to be very successful at evolving novel and unexpected ways of solving problems. In this work, we apply GP to the problem of predicting the outcomes of English Premier League games with the result being either win, lose or draw. We select 25 features from each game as the inputs to our GP system, which will then generate a function to predict the result. The experimental test on the prediction accuracy of a single GP-generated function is promising. One advantage of our GP system is, by implementing different runs or using different settings, it can generate as many high quality functions as we want. It has been showed that combining the decisions of a number of classifiers can provide better results than a single one. In this work, we combine 43 different GP-generated functions together and achieve significantly improved system performance.
{"title":"An ensemble based Genetic Programming system to predict English football premier league games","authors":"Tianxiang Cui, Jingpeng Li, J. Woodward, A. Parkes","doi":"10.1109/EAIS.2013.6604116","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604116","url":null,"abstract":"Predicting the result of a football game is challenging due to the complexity and uncertainties of many possible influencing factors involved. Genetic Programming (GP) has been shown to be very successful at evolving novel and unexpected ways of solving problems. In this work, we apply GP to the problem of predicting the outcomes of English Premier League games with the result being either win, lose or draw. We select 25 features from each game as the inputs to our GP system, which will then generate a function to predict the result. The experimental test on the prediction accuracy of a single GP-generated function is promising. One advantage of our GP system is, by implementing different runs or using different settings, it can generate as many high quality functions as we want. It has been showed that combining the decisions of a number of classifiers can provide better results than a single one. In this work, we combine 43 different GP-generated functions together and achieve significantly improved system performance.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"12 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":"114747342","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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604105
J. A. Iglesias, Agapito Ledezma, A. Sanchis
Humans often seek a second or third opinion about an important matter. Then, a final decision is reached after weighing and combining these opinions. This idea is the base of the ensemble based systems. Ensembles of classifiers are well established as a method for obtaining highly accurate classifiers by combining less accurate ones. On the other hand, evolving classifiers are inspired by the idea of evolve their structure in order to adapt to the changes of the environment. In this paper, we present a proof-of-concept method for constructing an ensemble system based on Evolving Fuzzy Systems. The main contribution of this approach is that the base-classifiers are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Thus, we present an ensemble system which is based on evolving classifiers and keeps the properties of the evolving approach classification of streaming data. It is important to clarify that the evolving classifiers are gradually developing but they are not genetic or evolutionary.
{"title":"Ensemble method based on individual evolving classifiers","authors":"J. A. Iglesias, Agapito Ledezma, A. Sanchis","doi":"10.1109/EAIS.2013.6604105","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604105","url":null,"abstract":"Humans often seek a second or third opinion about an important matter. Then, a final decision is reached after weighing and combining these opinions. This idea is the base of the ensemble based systems. Ensembles of classifiers are well established as a method for obtaining highly accurate classifiers by combining less accurate ones. On the other hand, evolving classifiers are inspired by the idea of evolve their structure in order to adapt to the changes of the environment. In this paper, we present a proof-of-concept method for constructing an ensemble system based on Evolving Fuzzy Systems. The main contribution of this approach is that the base-classifiers are self-developing (evolving) Fuzzy-rule-based (FRB) classifiers. Thus, we present an ensemble system which is based on evolving classifiers and keeps the properties of the evolving approach classification of streaming data. It is important to clarify that the evolving classifiers are gradually developing but they are not genetic or evolutionary.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"64 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":"126550424","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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604112
Mahardhika Pratama, S. Anavatti, M. Garratt, E. Lughofer
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.
{"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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604108
J. A. Iglesias, Agapito Ledezma, A. Sanchis
A computer can keep track of computer users to improve the security in the system. However, this does not prevent a user from impersonating another user. Only the user behavior recognition can help to detect masqueraders. Under the UNIX operating system, users type several commands which can be analyzed in order to create user profiles. These profiles identify a specific user or a specific computer user behavior. In addition, a computer user behavior changes over time. If the behavior recognition is done automatically, these changes need to be taken into account. For this reason, we propose in this paper a simple evolving method that is able to keep up to date the computer user behavior profiles. This method is based on Evolving Fuzzy Systems. The approach is evaluated using real data streams.
{"title":"Evolving systems for computer user behavior classification","authors":"J. A. Iglesias, Agapito Ledezma, A. Sanchis","doi":"10.1109/EAIS.2013.6604108","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604108","url":null,"abstract":"A computer can keep track of computer users to improve the security in the system. However, this does not prevent a user from impersonating another user. Only the user behavior recognition can help to detect masqueraders. Under the UNIX operating system, users type several commands which can be analyzed in order to create user profiles. These profiles identify a specific user or a specific computer user behavior. In addition, a computer user behavior changes over time. If the behavior recognition is done automatically, these changes need to be taken into account. For this reason, we propose in this paper a simple evolving method that is able to keep up to date the computer user behavior profiles. This method is based on Evolving Fuzzy Systems. The approach is evaluated using real data streams.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"58 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":"125490163","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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604117
D. Cartes, J. Chow, D. McCaugherty, S. Widergren, G. Venayagamoorthy
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.
{"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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604104
K. Subramanian, R. Savitha, S. Sundaram
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.
{"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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604110
Denis Kolev, P. Angelov, Garegin Markarian, M. Suvorov, S. Lysanov
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.
{"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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604101
Trevor P. Martin
Fuzzy formal concept analysis enables us to add structure to data by identifying coherent groups of related objects and attributes. In a situation where data is added dynamically, the concept lattice may evolve in different ways - either in content (more objects added to existing concepts) or in structure (entirely new concepts are created). This change can be monitored and quantified by means of a recently defined distance metric. In this paper, we present a new and more efficient algorithm for calculating the fuzzy distance between concept lattices, and illustrate the evolution of concept lattices by simple examples.
{"title":"Dynamic and evolving fuzzy concept lattices","authors":"Trevor P. Martin","doi":"10.1109/EAIS.2013.6604101","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604101","url":null,"abstract":"Fuzzy formal concept analysis enables us to add structure to data by identifying coherent groups of related objects and attributes. In a situation where data is added dynamically, the concept lattice may evolve in different ways - either in content (more objects added to existing concepts) or in structure (entirely new concepts are created). This change can be monitored and quantified by means of a recently defined distance metric. In this paper, we present a new and more efficient algorithm for calculating the fuzzy distance between concept lattices, and illustrate the evolution of concept lattices by simple examples.","PeriodicalId":289995,"journal":{"name":"2013 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"100 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":"122613967","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}
Pub Date : 2013-04-16DOI: 10.1109/EAIS.2013.6604115
C. Alt, H. A. Mayer
Digital organisms (DOs) model the basic structure and development of natural organisms to create robust, scalable, and adaptive solutions to problems from different fields. The applicability of DOs has been investigated mainly on a few synthetic problems like pattern creation, but on a very limited number of real world problems, e.g., the creation of architectural structures. In this paper the potential of DOs for learning to play the game of Go is demonstrated. Go has been chosen for its high complexity, its simple set of rules, and its pattern-oriented structure. A DO is designed, which is able to learn to play the game of Go by means of artificial evolution. The DO is evolved against three computer opponents of different strength on a 5×5 board. Specifically, we are interested in the DO's scalability, when evolved to play on the small board and transferred to a larger board without any external adaptations.
{"title":"Evolution of a digital organism playing Go","authors":"C. Alt, H. A. Mayer","doi":"10.1109/EAIS.2013.6604115","DOIUrl":"https://doi.org/10.1109/EAIS.2013.6604115","url":null,"abstract":"Digital organisms (DOs) model the basic structure and development of natural organisms to create robust, scalable, and adaptive solutions to problems from different fields. The applicability of DOs has been investigated mainly on a few synthetic problems like pattern creation, but on a very limited number of real world problems, e.g., the creation of architectural structures. In this paper the potential of DOs for learning to play the game of Go is demonstrated. Go has been chosen for its high complexity, its simple set of rules, and its pattern-oriented structure. A DO is designed, which is able to learn to play the game of Go by means of artificial evolution. The DO is evolved against three computer opponents of different strength on a 5×5 board. Specifically, we are interested in the DO's scalability, when evolved to play on the small board and transferred to a larger board without any external adaptations.","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":"131031736","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}
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