Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295515
Hakim Lamara, L. Vermeiren, D. Roger
This paper follows on from some works achieved in a team of the LAMIH, entitled "systems modeling and control group". This ones deal with a fuzzy arithmetic whose first interest is to be more practical than the extension principle one and alpha-cut based methods. It comes from a different representation of fuzzy numbers. The arithmetic proposed can be extended to most of the fuzzy quantities. The present paper follows up work in introducing a metric for fuzzy numbers, a weighted fuzzy fusion operator and its application to data analysis and filtering.
{"title":"Aggregation and fuzzification by weighted fuzzy fusion operator","authors":"Hakim Lamara, L. Vermeiren, D. Roger","doi":"10.1109/FUZZY.2007.4295515","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295515","url":null,"abstract":"This paper follows on from some works achieved in a team of the LAMIH, entitled \"systems modeling and control group\". This ones deal with a fuzzy arithmetic whose first interest is to be more practical than the extension principle one and alpha-cut based methods. It comes from a different representation of fuzzy numbers. The arithmetic proposed can be extended to most of the fuzzy quantities. The present paper follows up work in introducing a metric for fuzzy numbers, a weighted fuzzy fusion operator and its application to data analysis and filtering.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126531999","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295373
Jérica Urías, P. Melin, O. Castillo
We describe in this paper a new method for response integration in modular neural networks using type-2 fuzzy logic. The modular neural networks were used in human person recognition. Biometric authentication is used to achieve person recognition. Three biometric characteristics of the person are used: face, fingerprint, and voice. A modular neural network of three modules is used. Each module is a local expert on person recognition based on each of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper the results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.
{"title":"A Method for Response Integration in Modular Neural Networks using Interval Type-2 Fuzzy Logic","authors":"Jérica Urías, P. Melin, O. Castillo","doi":"10.1109/FUZZY.2007.4295373","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295373","url":null,"abstract":"We describe in this paper a new method for response integration in modular neural networks using type-2 fuzzy logic. The modular neural networks were used in human person recognition. Biometric authentication is used to achieve person recognition. Three biometric characteristics of the person are used: face, fingerprint, and voice. A modular neural network of three modules is used. Each module is a local expert on person recognition based on each of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper the results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127408755","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295411
J. Kacprzyk, A. Wilbik, S. Zadrożny
We extend our approach to the linguistic summarization of (numerical) time series. The main issue boils down to the identification of trends in time series that are characterized by a set of attributes followed by their appropriate aggregation. We propose to use the OWA (ordered weighted averaging) operators for the aggregation of partial trends as an alternative to the use of the classic Zadeh's calculus of linguistically quantified propositions, the Sugeno integral and the Choquet integral. The use of the OWA operators provides a convenient unified aggregation means that can be used to derive diverse types of summaries. The results obtained confirm a high human consistency of linguistic summaries derived.
{"title":"Linguistic Summaries of Time Series via an OWA Operator Based Aggregation of Partial Trends","authors":"J. Kacprzyk, A. Wilbik, S. Zadrożny","doi":"10.1109/FUZZY.2007.4295411","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295411","url":null,"abstract":"We extend our approach to the linguistic summarization of (numerical) time series. The main issue boils down to the identification of trends in time series that are characterized by a set of attributes followed by their appropriate aggregation. We propose to use the OWA (ordered weighted averaging) operators for the aggregation of partial trends as an alternative to the use of the classic Zadeh's calculus of linguistically quantified propositions, the Sugeno integral and the Choquet integral. The use of the OWA operators provides a convenient unified aggregation means that can be used to derive diverse types of summaries. The results obtained confirm a high human consistency of linguistic summaries derived.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127454558","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295453
Luciano Stefanini
The generalized differentiability of a fuzzy-number-valued function of a real variable, as recently introduced by Bede and Gal (Fuzzy Sets and Systems, vol. 151, 2005), can be expressed by first defining a generalized Hukuhara difference and using it for the differentiability; to do so, the basic elements are the lower and upper functions which characterize the level-cuts of the fuzzy quantities i.e. functions that are monotonic over [0,1]. Using this fact, we present a (parametric) representation of fuzzy numbers and its application to the solution of fuzzy differential (initial value) equations (FDE). The representation uses a finite decomposition of the membership interval [0,1] and models the level-cuts of fuzzy numbers and fuzzy functions to obtain the formulation of a fuzzy differential equation y'=f(x,y) in terms of a set of ordinary (non fuzzy) differential equations, defined by the lower and upper components of the fuzzy-valued function f(x,y). From a computational view, the resulting ODE's can be analyzed and solved by standard methods of numerical analysis.
Bede和Gal (Fuzzy Sets and Systems, vol. 151, 2005)最近引入了实变量的模糊数值函数的广义可微性，可以通过首先定义广义Hukuhara差分并将其用于可微性来表示;要做到这一点，基本元素是表征模糊量的水平切割的下函数和上函数，即在[0,1]上单调的函数。利用这一事实，我们给出了模糊数的参数表示及其在模糊微分初值方程(FDE)求解中的应用。该表示使用隶属度区间[0,1]的有限分解，并对模糊数和模糊函数的水平切进行建模，得到由模糊值函数f(x,y)的上下分量定义的一组普通(非模糊)微分方程的模糊微分方程y'=f(x,y)的表达式。从计算的角度来看，所得的ODE可以用数值分析的标准方法进行分析和求解。
{"title":"On the generalized LU-fuzzy derivative and fuzzy differential equations","authors":"Luciano Stefanini","doi":"10.1109/FUZZY.2007.4295453","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295453","url":null,"abstract":"The generalized differentiability of a fuzzy-number-valued function of a real variable, as recently introduced by Bede and Gal (Fuzzy Sets and Systems, vol. 151, 2005), can be expressed by first defining a generalized Hukuhara difference and using it for the differentiability; to do so, the basic elements are the lower and upper functions which characterize the level-cuts of the fuzzy quantities i.e. functions that are monotonic over [0,1]. Using this fact, we present a (parametric) representation of fuzzy numbers and its application to the solution of fuzzy differential (initial value) equations (FDE). The representation uses a finite decomposition of the membership interval [0,1] and models the level-cuts of fuzzy numbers and fuzzy functions to obtain the formulation of a fuzzy differential equation y'=f(x,y) in terms of a set of ordinary (non fuzzy) differential equations, defined by the lower and upper components of the fuzzy-valued function f(x,y). From a computational view, the resulting ODE's can be analyzed and solved by standard methods of numerical analysis.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176448","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295394
A. Khosravi, Joaquim Armengol Llobet, E. Gelso
Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately.
{"title":"An Interval Intelligent-based Approach for Fault Detection and Modelling","authors":"A. Khosravi, Joaquim Armengol Llobet, E. Gelso","doi":"10.1109/FUZZY.2007.4295394","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295394","url":null,"abstract":"Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130332082","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295472
J. E. M. Expósito, S. G. Galán, Nicolas Ruiz Reyes, P. V. Candeas
Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.
{"title":"Audio Coding Improvement Using Evolutionary Speech/Music Discrimination","authors":"J. E. M. Expósito, S. G. Galán, Nicolas Ruiz Reyes, P. V. Candeas","doi":"10.1109/FUZZY.2007.4295472","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295472","url":null,"abstract":"Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130625759","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295447
Ma Nam, Nguyen T. B. Ngoc, Hoa Nguyen, T. Cao
For modelling real-world problems and constructing intelligent systems, integration of different methodologies and techniques has been the quest and focus of significant interdisciplinary research effort. The advantages of such a hybrid system are that the strengths of its partners are combined and complementary to each other's weakness. However, extended object-oriented models that combine the relevance and strength of both fuzzy set theory and probability theory appear to be sporadic. Furthermore, the soft computing paradigm needs to have real systems implemented to be useful in practice. This paper presents our development of FPDB40 as a management system for fuzzy and probabilistic object bases of the model called FPOB. The syntax and semantics of FPOB schemas, instances, and selection operation are summarized. Then the implementation of those features in FPDB40 is presented.
{"title":"FPDB40: A Fuzzy and Probabilistic Object Base Management System","authors":"Ma Nam, Nguyen T. B. Ngoc, Hoa Nguyen, T. Cao","doi":"10.1109/FUZZY.2007.4295447","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295447","url":null,"abstract":"For modelling real-world problems and constructing intelligent systems, integration of different methodologies and techniques has been the quest and focus of significant interdisciplinary research effort. The advantages of such a hybrid system are that the strengths of its partners are combined and complementary to each other's weakness. However, extended object-oriented models that combine the relevance and strength of both fuzzy set theory and probability theory appear to be sporadic. Furthermore, the soft computing paradigm needs to have real systems implemented to be useful in practice. This paper presents our development of FPDB40 as a management system for fuzzy and probabilistic object bases of the model called FPOB. The syntax and semantics of FPOB schemas, instances, and selection operation are summarized. Then the implementation of those features in FPDB40 is presented.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130707273","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295595
R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera
Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions. Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic label membership functions. Based on the 2-tuples linguistic representation model, we present a new fuzzy data-mining algorithm for extracting both association rules and membership functions by means of an evolutionary learning of the membership functions, using a basic method for mining fuzzy association rules.
{"title":"Genetic Learning of Membership Functions for Mining Fuzzy Association Rules","authors":"R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera","doi":"10.1109/FUZZY.2007.4295595","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295595","url":null,"abstract":"Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions. Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic label membership functions. Based on the 2-tuples linguistic representation model, we present a new fuzzy data-mining algorithm for extracting both association rules and membership functions by means of an evolutionary learning of the membership functions, using a basic method for mining fuzzy association rules.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117037013","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295591
R. A. Assi, S. Kaci
Preferences, which control our decisions in the daily life, have been widely studied and analyzed in computer science. In artificial intelligence, preferences are used in many domains such as decision theory, learning, etc. Several representations and reasoning techniques of preferences were proposed. One of these representations is the non-monotonic logic of preferences characterized by the ability to express several interpretations of preferences simultaneously. In relational databases, preferences are used for the personalization of queries to reduce the volume of data presented to the user by offering only the information that interests him. There, preferences are typically specified using binary preference relations among tuples. Binary preference relations are defined by preference formulas which can be embedded into classical relational queries. This paper is intended to discuss the encoding of relational database preference queries in the framework of the non-monotonic logic of preferences. We show that this framework allows the representation of binary preference relations that are asymmetric orders. In addition, it provides several mechanisms to manipulate preference queries efficiently.
{"title":"A Compact Representation of Preference Queries","authors":"R. A. Assi, S. Kaci","doi":"10.1109/FUZZY.2007.4295591","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295591","url":null,"abstract":"Preferences, which control our decisions in the daily life, have been widely studied and analyzed in computer science. In artificial intelligence, preferences are used in many domains such as decision theory, learning, etc. Several representations and reasoning techniques of preferences were proposed. One of these representations is the non-monotonic logic of preferences characterized by the ability to express several interpretations of preferences simultaneously. In relational databases, preferences are used for the personalization of queries to reduce the volume of data presented to the user by offering only the information that interests him. There, preferences are typically specified using binary preference relations among tuples. Binary preference relations are defined by preference formulas which can be embedded into classical relational queries. This paper is intended to discuss the encoding of relational database preference queries in the framework of the non-monotonic logic of preferences. We show that this framework allows the representation of binary preference relations that are asymmetric orders. In addition, it provides several mechanisms to manipulate preference queries efficiently.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131015114","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295631
R. Giráldez, F. Divina, Beatriz Pontes, J. Aguilar-Ruiz
Biclustering techniques aim at extracting significant subsets of genes and conditions from microarray gene expression data. This kind of algorithms is mainly based on two key aspects: the way in which they deal with gene similarity across the experimental conditions, that determines the quality of biclusters; and the heuristic or search strategy used for exploring the search space. A measure that is often adopted for establishing the quality of biclusters is the mean squared residue. This measure has been successfully used in many approaches. However, it has been recently proven that the mean squared residue fails to recognize some kind of biclusters as quality biclusters, mainly due to the difficulty of detecting scaling patterns in data. In this work, we propose a novel measure for trying to overcome this drawback. This measure is based on the area between two curves. Such curves are built from the maximum and minimum standardized expression values exhibited for each experimental condition. In order to test the proposed measure, we have incorporated it into a multiobjective evolutionary algorithm. Experimental results confirm the effectiveness of our approach. The combination of the measure we propose with the mean squared residue yields results that would not have been obtained if only the mean squared residue had been used.
{"title":"Evolutionary Search of Biclusters by Minimal Intrafluctuation","authors":"R. Giráldez, F. Divina, Beatriz Pontes, J. Aguilar-Ruiz","doi":"10.1109/FUZZY.2007.4295631","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295631","url":null,"abstract":"Biclustering techniques aim at extracting significant subsets of genes and conditions from microarray gene expression data. This kind of algorithms is mainly based on two key aspects: the way in which they deal with gene similarity across the experimental conditions, that determines the quality of biclusters; and the heuristic or search strategy used for exploring the search space. A measure that is often adopted for establishing the quality of biclusters is the mean squared residue. This measure has been successfully used in many approaches. However, it has been recently proven that the mean squared residue fails to recognize some kind of biclusters as quality biclusters, mainly due to the difficulty of detecting scaling patterns in data. In this work, we propose a novel measure for trying to overcome this drawback. This measure is based on the area between two curves. Such curves are built from the maximum and minimum standardized expression values exhibited for each experimental condition. In order to test the proposed measure, we have incorporated it into a multiobjective evolutionary algorithm. Experimental results confirm the effectiveness of our approach. The combination of the measure we propose with the mean squared residue yields results that would not have been obtained if only the mean squared residue had been used.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131282177","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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