Pub Date : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.63
Fabricio A. Breve, Liang Zhao
Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.
概念漂移是指随着时间推移的非平稳学习问题，在机器学习和数据挖掘中越来越重要。许多概念漂移应用需要快速响应，这意味着算法必须始终使用最新可用数据进行(重新)训练。但是，与获取未标记的数据相比，数据标记的过程通常是昂贵的和/或耗时的，因此通常只有一小部分传入数据可以有效地标记。在这种情况下，半监督学习方法可能会有所帮助，因为它们在训练过程中同时使用标记和未标记的数据。然而，它们中的大多数都是基于数据是静态的假设。因此，带有概念漂移的半监督学习仍然是机器学习中一个开放的具有挑战性的任务。近年来，人们提出了一种粒子竞争与合作的方法来实现基于图的静态数据半监督学习。我们已经扩展了这种方法来处理数据流和概念漂移。结果是使用单一分类器方法的被动算法，自然地适应概念变化，没有任何显式的漂移检测机制。它具有内置的机制，提供了一种从新数据中学习的自然方式，随着旧数据项对新数据项的分类不再有用，逐渐“忘记”旧知识。将该算法应用于KDD Cup 1999网络入侵数据，验证了算法的有效性。
{"title":"Semi-supervised Learning with Concept Drift Using Particle Dynamics Applied to Network Intrusion Detection Data","authors":"Fabricio A. Breve, Liang Zhao","doi":"10.1109/BRICS-CCI-CBIC.2013.63","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.63","url":null,"abstract":"Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually \"forgetting\" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125368968","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.84
Erik Alexandre Pucci, Aurora Trinidad Ramirez Pozo, E. Spinosa
Estimation of Distribution Algorithms (EDA) are stochastic population based search algorithms that use a distribution model of the population to create new candidate solutions. One problem that directly affects the EDAs' ability to find the best solutions is the premature convergence to some local optimum due to diversity loss. Inspired by the Random Immigrants technique, this paper presents the Bayesian Optimization Algorithm with Random Immigration (BOARI). The algorithm generates and migrates random individuals as a way to improve the performance of the Bayesian Optimization Algorithm (BOA) by maintaining the genetic diversity of the population along the generations. The proposed approach has been evaluated and compared to BOA using benchmark functions. Results indicate that, with appropriate settings, the algorithm is able to achieve better solutions than the standard BOA for these functions.
{"title":"Bayesian Optimization Algorithm with Random Immigration","authors":"Erik Alexandre Pucci, Aurora Trinidad Ramirez Pozo, E. Spinosa","doi":"10.1109/BRICS-CCI-CBIC.2013.84","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.84","url":null,"abstract":"Estimation of Distribution Algorithms (EDA) are stochastic population based search algorithms that use a distribution model of the population to create new candidate solutions. One problem that directly affects the EDAs' ability to find the best solutions is the premature convergence to some local optimum due to diversity loss. Inspired by the Random Immigrants technique, this paper presents the Bayesian Optimization Algorithm with Random Immigration (BOARI). The algorithm generates and migrates random individuals as a way to improve the performance of the Bayesian Optimization Algorithm (BOA) by maintaining the genetic diversity of the population along the generations. The proposed approach has been evaluated and compared to BOA using benchmark functions. Results indicate that, with appropriate settings, the algorithm is able to achieve better solutions than the standard BOA for these functions.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123071764","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.117
Vinicius da F Vieira, C. R. Xavier, Alexandre Evsukoff
One of the most important features of a network is its division into communities, groups of nodes with many internal and few external connections. Furthermore, the community structure of a network can be organized hierarchically, which reflects a natural behavior of real life phenomena. It is a difficult task to detect and understand the community structure of a network and it becomes even more challenging as data availability (and networks sizes) increases. This work presents a efficient implementation for community detection in networks aiming on modularity maximization based on the Newman's spectral method with a fine tuning(FT) stage. This work presents a modification on the FT which substantially reduces the execution time, while preserving the division quality. A high performance implementation of the method enables their application to large real world networks. The Newman's spectral method can be applied to networks with more than 1 million nodes in a personal computer.
{"title":"Efficient Community Detection in Large Scale Networks","authors":"Vinicius da F Vieira, C. R. Xavier, Alexandre Evsukoff","doi":"10.1109/BRICS-CCI-CBIC.2013.117","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.117","url":null,"abstract":"One of the most important features of a network is its division into communities, groups of nodes with many internal and few external connections. Furthermore, the community structure of a network can be organized hierarchically, which reflects a natural behavior of real life phenomena. It is a difficult task to detect and understand the community structure of a network and it becomes even more challenging as data availability (and networks sizes) increases. This work presents a efficient implementation for community detection in networks aiming on modularity maximization based on the Newman's spectral method with a fine tuning(FT) stage. This work presents a modification on the FT which substantially reduces the execution time, while preserving the division quality. A high performance implementation of the method enables their application to large real world networks. The Newman's spectral method can be applied to networks with more than 1 million nodes in a personal computer.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"329 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116123383","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.87
Rafael Xavier Valente, Antonio Braga, W. Pedrycz
This paper presents a new validity index for fuzzy partitions generated by the fuzzy c-means algorithm. The proposed validity index is based on the calculation of factors from the proximity matrix generated from the membership matrix generated by a fuzzy clustering partition algorithm, such as FCM. The experimental results show that the proposed approach is consistent with other well-known metrics and with the dataset structure as observed from Proximity Matrices.
{"title":"A New Fuzzy Clustering Validity Index Based on Fuzzy Proximity Matrices","authors":"Rafael Xavier Valente, Antonio Braga, W. Pedrycz","doi":"10.1109/BRICS-CCI-CBIC.2013.87","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.87","url":null,"abstract":"This paper presents a new validity index for fuzzy partitions generated by the fuzzy c-means algorithm. The proposed validity index is based on the calculation of factors from the proximity matrix generated from the membership matrix generated by a fuzzy clustering partition algorithm, such as FCM. The experimental results show that the proposed approach is consistent with other well-known metrics and with the dataset structure as observed from Proximity Matrices.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122458175","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.52
Jobson Renan, J. Silva, S. Galdino
This paper introduces two new approaches to fit univariate resistant linear regression models on interval-valued data. Linear regressions on interval-valued data gives point predictions. The prediction of the lower and upper bounds from interval-valued data of dependent variable are estimated from the fitted range resistant linear regression model. The new proposed methods should be used in presence of outliers.
{"title":"Resistant Regression for Interval-Valued Data","authors":"Jobson Renan, J. Silva, S. Galdino","doi":"10.1109/BRICS-CCI-CBIC.2013.52","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.52","url":null,"abstract":"This paper introduces two new approaches to fit univariate resistant linear regression models on interval-valued data. Linear regressions on interval-valued data gives point predictions. The prediction of the lower and upper bounds from interval-valued data of dependent variable are estimated from the fitted range resistant linear regression model. The new proposed methods should be used in presence of outliers.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122838965","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.27
D. A. Augusto, H. Bernardino, H. Barbosa
Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques, a real-world problem of meritocracy in jobs by evolving classifiers that were both accurate and human-readable.
{"title":"Predicting the Performance of Job Applicants by Means of Genetic Programming","authors":"D. A. Augusto, H. Bernardino, H. Barbosa","doi":"10.1109/BRICS-CCI-CBIC.2013.27","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.27","url":null,"abstract":"Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques, a real-world problem of meritocracy in jobs by evolving classifiers that were both accurate and human-readable.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132916784","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.54
T. C. Silva, Liang Zhao
Traditional data classification considers only physical features (e.g., geometrical or statistical features) of the input data. Here, it is referred to low level classification. In contrast, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is here called high level classification. In this paper, we present an alternative technique which combines both low and high level data classification techniques. The low level term can be implemented by any classification technique, while the high level term is realized by means of the extraction of the underlying network's features (graph) constructed from the input data, which measures the compliance of the test instances with the pattern formation of the training data. Out of various high level perspectives that can be utilized to capture semantical meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment. Specifically, a weighted combination of transient and cycle lengths are employed for that end. Furthermore, we show computer simulations with synthetic and widely accepted real-world data sets from the machine learning literature. Interestingly, our study shows that the proposed technique is able to further improve the already optimized performance of traditional classification techniques.
{"title":"Pattern-Based Classification via a High Level Approach Using Tourist Walks in Networks","authors":"T. C. Silva, Liang Zhao","doi":"10.1109/BRICS-CCI-CBIC.2013.54","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.54","url":null,"abstract":"Traditional data classification considers only physical features (e.g., geometrical or statistical features) of the input data. Here, it is referred to low level classification. In contrast, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is here called high level classification. In this paper, we present an alternative technique which combines both low and high level data classification techniques. The low level term can be implemented by any classification technique, while the high level term is realized by means of the extraction of the underlying network's features (graph) constructed from the input data, which measures the compliance of the test instances with the pattern formation of the training data. Out of various high level perspectives that can be utilized to capture semantical meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment. Specifically, a weighted combination of transient and cycle lengths are employed for that end. Furthermore, we show computer simulations with synthetic and widely accepted real-world data sets from the machine learning literature. Interestingly, our study shows that the proposed technique is able to further improve the already optimized performance of traditional classification techniques.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134514578","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.38
Diego F. P. De Souza, Hugo C. C. Carneiro, F. França, P. Lima
This paper presents some strategies used for creating intelligent players of rock-paper-scissors using WiSARD weightless neural networks and results obtained therewith. These strategies included: (i) a new approach for encoding of the input data, (ii) three new training algorithms that allow the reclassification of the input patterns over time, (iii) a method for dealing with incomplete information in the input array, and (iv) a bluffing strategy. Experiments show that, in a tournament of intelligent agents, WiSARD-based agents were ranked among the 200 best players, one of them achieving 9th place for about three weeks.
{"title":"Rock-Paper-Scissors WiSARD","authors":"Diego F. P. De Souza, Hugo C. C. Carneiro, F. França, P. Lima","doi":"10.1109/BRICS-CCI-CBIC.2013.38","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.38","url":null,"abstract":"This paper presents some strategies used for creating intelligent players of rock-paper-scissors using WiSARD weightless neural networks and results obtained therewith. These strategies included: (i) a new approach for encoding of the input data, (ii) three new training algorithms that allow the reclassification of the input patterns over time, (iii) a method for dealing with incomplete information in the input array, and (iv) a bluffing strategy. Experiments show that, in a tournament of intelligent agents, WiSARD-based agents were ranked among the 200 best players, one of them achieving 9th place for about three weeks.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131531974","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.50
David Iclanzan, Camelia Chira
The complex regimes of operation situated between ordered and chaotic behavior are hypothesized to give rise to computational capabilities. Lacking an universal blueprint for the emergence of complexity, a costly search is typically used to find the configurations of distributed artificial systems that can facilitate global computation. In this paper, we address the tedious task of searching for complex cellular automata rules able to lead to a certain global behavior based on local interactions. The discovery of rules exhibiting a high degree of global self-organization is of major importance in the study and understanding of complex systems. A classical heuristic search guided only by a coarse approximation of the ability of a rule to perform in certain conditions will generally not reach beyond an ordered regime of operation. To overcome this limitation, in this paper we incorporate a promising heuristic that rewards increased dynamics with regard to cell state changes in a multiobjective, parallel evolutionary framework. The scope of the multiobjective formulation is to balance the search between ordered and chaotic regimes in order to facilitate the discovery of rules exhibiting complex behaviors. Experimental results confirm that the combined approach represents an efficient way for supporting the emergence of complexity as in all runs we were able to find cellular automata exhibiting a high degree of global self-organization.
{"title":"A Parallel Multiobjective Approach to Evolving Cellular Automata Rules by Cell State Change Dynamics","authors":"David Iclanzan, Camelia Chira","doi":"10.1109/BRICS-CCI-CBIC.2013.50","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.50","url":null,"abstract":"The complex regimes of operation situated between ordered and chaotic behavior are hypothesized to give rise to computational capabilities. Lacking an universal blueprint for the emergence of complexity, a costly search is typically used to find the configurations of distributed artificial systems that can facilitate global computation. In this paper, we address the tedious task of searching for complex cellular automata rules able to lead to a certain global behavior based on local interactions. The discovery of rules exhibiting a high degree of global self-organization is of major importance in the study and understanding of complex systems. A classical heuristic search guided only by a coarse approximation of the ability of a rule to perform in certain conditions will generally not reach beyond an ordered regime of operation. To overcome this limitation, in this paper we incorporate a promising heuristic that rewards increased dynamics with regard to cell state changes in a multiobjective, parallel evolutionary framework. The scope of the multiobjective formulation is to balance the search between ordered and chaotic regimes in order to facilitate the discovery of rules exhibiting complex behaviors. Experimental results confirm that the combined approach represents an efficient way for supporting the emergence of complexity as in all runs we were able to find cellular automata exhibiting a high degree of global self-organization.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132346008","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 : 2013-09-08DOI: 10.1109/BRICS-CCI-CBIC.2013.98
Wanessa da Silva, M. Habermann, Elcio Hideiti Shiguemori, Leidiane do Livramento Andrade, Ruy Morgado de Castro
This work presents a methodology for pattern classification from multispectral images acquired by the HSS airborne sensor. In order to achieve this purpose, a conjunction of Artificial Neural Network and Principal Components Analysis has been used. The results indicate that this approach can be alternatively employed in multispectral images to separate materials with specific characteristics based on their reflectance properties.
{"title":"Multispectral Image Classification Using Multilayer Perceptron and Principal Components Analysis","authors":"Wanessa da Silva, M. Habermann, Elcio Hideiti Shiguemori, Leidiane do Livramento Andrade, Ruy Morgado de Castro","doi":"10.1109/BRICS-CCI-CBIC.2013.98","DOIUrl":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.98","url":null,"abstract":"This work presents a methodology for pattern classification from multispectral images acquired by the HSS airborne sensor. In order to achieve this purpose, a conjunction of Artificial Neural Network and Principal Components Analysis has been used. The results indicate that this approach can be alternatively employed in multispectral images to separate materials with specific characteristics based on their reflectance properties.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132478129","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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