2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence最新文献_第2页

Semi-supervised Learning with Concept Drift Using Particle Dynamics Applied to Network Intrusion Detection Data 基于粒子动力学的概念漂移半监督学习在网络入侵检测数据中的应用

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.63

Fabricio A. Breve, Liang Zhao

{"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}

引用次数: 12

Bayesian Optimization Algorithm with Random Immigration 随机迁移贝叶斯优化算法

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.84

Erik Alexandre Pucci, Aurora Trinidad Ramirez Pozo, E. Spinosa

引用次数: 0

Efficient Community Detection in Large Scale Networks 大规模网络中的高效社群检测

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.117

Vinicius da F Vieira, C. R. Xavier, Alexandre Evsukoff

引用次数: 1

A New Fuzzy Clustering Validity Index Based on Fuzzy Proximity Matrices 一种新的基于模糊接近矩阵的模糊聚类有效性指标

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.87

Rafael Xavier Valente, Antonio Braga, W. Pedrycz

引用次数: 2

Resistant Regression for Interval-Valued Data 区间值数据的抵抗回归

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.52

Jobson Renan, J. Silva, S. Galdino

引用次数: 0

Predicting the Performance of Job Applicants by Means of Genetic Programming 基于遗传规划的求职者绩效预测

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.27

D. A. Augusto, H. Bernardino, H. Barbosa

引用次数: 4

Pattern-Based Classification via a High Level Approach Using Tourist Walks in Networks 基于模式的高级分类方法在旅游网络中的应用

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.54

T. C. Silva, Liang Zhao

{"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}

引用次数: 3

Bi-dimensional Neural Equalizer Applied to Optical Receiver 二维神经均衡器在光接收机中的应用

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.17

Tiago F. B. de Sousa, Marcelo A. C. Fernandes

引用次数: 1

Use of Statistical Control for Improved Demand Forecasting 使用统计控制改进需求预测

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.121

E. Christo, M. Ferreira, K. C. Alonso

引用次数: 2

Monitoring Diesel Fuels with Supervised Distance Preserving Projections and Local Linear Regression 用监督距离保持投影和局部线性回归监测柴油燃料

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence Pub Date : 2013-09-08 DOI: 10.1109/BRICS-CCI-CBIC.2013.76

F. Corona, Zhanxing Zhu, Amauri H. Souza Junior, M. Mulas, G. Barreto, R. Baratti

引用次数: 0