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2010 International Conference of Soft Computing and Pattern Recognition最新文献

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Improving the accuracy of intrusion detection systems by using the combination of machine learning approaches 结合机器学习方法提高入侵检测系统的准确性
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686163
H. Sarvari, M. M. Keikha
{"title":"Improving the accuracy of intrusion detection systems by using the combination of machine learning approaches","authors":"H. Sarvari, M. M. Keikha","doi":"10.1109/SOCPAR.2010.5686163","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686163","url":null,"abstract":"An Intrusion detection system is designed to classify the system activities into normal and abnormal. We use a combination of machine learning approaches as to detect the system attacks. The experimental results of the study show that increasing the number of classifiers has a threshold limit and the system accuracy will remain constant if the number of classifiers goes beyond this limit. The determination of the threshold limit is tentative. This article, also, presents a solution for unbalanced data of some attacks. The comparison of the results with other similar articles proves the efficiency of the presented system.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114547144","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: 19
A discrete particle swarm optimization with random selection solution for the shortest path problem 最短路径问题的随机选择离散粒子群优化
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685867
M. Yusoff, J. Ariffin, A. Mohamed
{"title":"A discrete particle swarm optimization with random selection solution for the shortest path problem","authors":"M. Yusoff, J. Ariffin, A. Mohamed","doi":"10.1109/SOCPAR.2010.5685867","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685867","url":null,"abstract":"This article proposes a discrete particle swarm optimization (DPSO) for solution of the shortest path problem (SPP). The proposed DPSO adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Detailed descriptions of the new solution and the DPSO algorithm are elaborated. Computational experiments involve an SPP dataset from previous research and road network from Malaysia. The DPSO is compared with a genetic algorithm (GA) using naive and new solution mapping. The results indicate that the proposed DPSO is highly competitive and shows good performance in both fitness value and processing time.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123494803","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: 10
A generative solution for ATM cash management ATM现金管理的生成式解决方案
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686730
Roberto Armenise, Cosimo Birtolo, Eugenio Sangianantoni, L. Troiano
{"title":"A generative solution for ATM cash management","authors":"Roberto Armenise, Cosimo Birtolo, Eugenio Sangianantoni, L. Troiano","doi":"10.1109/SOCPAR.2010.5686730","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686730","url":null,"abstract":"Optimizing cash in automatic teller machines (ATM) is challenging due unpredictability of withdrawals, but profitable because of the large number of tellers. Generally ATM cash management and optimization is performed manually, according to corporate policies and personnel experience. A non-optimal cash upload can lead to poor service when cash demand is underestimated and to unnecessary costs when demand is overestimated. Therefore, finding the best match between cash stock and demand becomes crucial to improve. Recently, some authors attempted to optimize the cash by modeling and forecasting the demand. However, the high variance and non-stationarity of the underlying stochastic process can affect reliability of such an approach. In this paper we suggest the application of genetic algorithms as means for searching and generating optimal upload strategies, able at the same time to minimize the daily amount of stocked money and to assure cash dispensing service. Experimentation led at Poste Italiane S.p.A. makes this promising and worth to be further investigated.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125871886","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: 15
Solving data fusion problems using robust classifiers combination 利用鲁棒分类器组合解决数据融合问题
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686639
Afef Ben Brahim, Riadh Khanchel, M. Limam
{"title":"Solving data fusion problems using robust classifiers combination","authors":"Afef Ben Brahim, Riadh Khanchel, M. Limam","doi":"10.1109/SOCPAR.2010.5686639","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686639","url":null,"abstract":"In classification problems where additional information is obtained from different data sources having heterogeneous features, a classifier trained earlier on the original data can not be updated to learn new data. One solution is to fuse this data by using classifiers to learn from each source and to combine outputs in order to get a more accurate final decision. Ensemble methods are well suited to solve data fusion problems since they use all data sources available while taking advantage of complementary information. Robust methods are proposed for combining classifiers, aimed at reducing the effect of outlier classifiers in the ensemble. The proposed methods are shown to have better performance leading to significantly better classification results than the previously employed techniques.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126387153","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: 0
A DCT based approach for detecting novelty and concept drift in data streams 一种基于DCT的数据流新颖性和概念漂移检测方法
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686734
Morteza Zi Hayat, M. Hashemi
{"title":"A DCT based approach for detecting novelty and concept drift in data streams","authors":"Morteza Zi Hayat, M. Hashemi","doi":"10.1109/SOCPAR.2010.5686734","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686734","url":null,"abstract":"Data streams are one of the most challenging environments for machine learning. In many applications, the high volume data streams have an inherent concept drift over time. Identifying novel classes and detecting the occurrence of concept drift in such an environment is a major challenge. In this paper, a new method has been proposed to detect novelty and handle concept drift with limited required memory and storage space. The method is based on clustering algorithm. It uses Discrete Cosine Transform to build compact generative models which are then used to detect novel classes and concept drift effectively. The proposed method has been evaluated with seven common data sets from various domains. The results indicate its superior performance when compared with existing methods in terms of novelty and drift detection, computational complexity and memory requirements.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125904563","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: 44
Flexible optimization of text recognition algorithms 文本识别算法的灵活优化
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685975
Britta Meixner, F. Pein, H. Kosch
{"title":"Flexible optimization of text recognition algorithms","authors":"Britta Meixner, F. Pein, H. Kosch","doi":"10.1109/SOCPAR.2010.5685975","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685975","url":null,"abstract":"This paper presents a system for the optimization of text recognition algorithms. First a theoretic four-staged model of text recognition is proposed. In this four-staged model, the second stage called text localization is optimized. A reinterpreted version of the F measure is used as a fitness indicator for optimization of the localization. The optimization method is described and the role of the algorithm of Nelder and Mead in the optimization process is explained. The system is introduced and it is indicated, how it can be extended with custom algorithms. Selected experimental results are presented at the end of this work. The optimization approach can improve existing text localization algorithms on untrained data up to 87% of their base localization rate in F measure category.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130602670","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: 0
Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms 遗传算法在股票交易策略获取中的邻域评价
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686733
K. Matsui, Haruo Sato
{"title":"Neighborhood evaluation in acquiring stock trading strategy using genetic algorithms","authors":"K. Matsui, Haruo Sato","doi":"10.1109/SOCPAR.2010.5686733","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686733","url":null,"abstract":"We propose a new method to evaluate individuals in genetic algorithms (GAs) for algorithmic trading in stock markets. In our previous work, we presented an effective method to acquire trading strategy in stock markets. However, it had a tendency of overfitting in genetic searches. Our new approach, namely neighborhood evaluation, involves evaluation for neighboring points of genetic individuals in fitness landscape as well as themselves. We examine the performance of our method in stock trading of twenty companies in the first section of Tokyo Stock Exchange for recent eleven years, and show the effectiveness of the neighborhood evaluation.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127311920","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: 22
Fuzzy gradual patterns: What fuzzy modality for what result? 模糊渐进模式:什么样的模糊模式导致什么样的结果?
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686082
S. Ayouni, S. Yahia, Anne Laurent, P. Poncelet
{"title":"Fuzzy gradual patterns: What fuzzy modality for what result?","authors":"S. Ayouni, S. Yahia, Anne Laurent, P. Poncelet","doi":"10.1109/SOCPAR.2010.5686082","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686082","url":null,"abstract":"Gradual Patterns allow to express frequent patterns like “the older an employee, the higher his/her income”. After having been extensively used for fuzzy command (e.g., “the closer the wall, the harder the break”), they are currently grasping the attention of the data mining community, where the focus is put on the scalability of the methods when addressing very large databases (e.g. biology). However, it is often the case that the information is not contained at the upper and lower boundaries (min/max), but rather hidden in-between. For instance, the most interesting rule could be “the closer the age of an employee to 46, the higher his/her income”. In this paper, we introduce several methods for automatically defining fuzzy modalities and we compare the results obtained when considering the various solutions.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130946684","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: 19
Osteosarcoma segmentation in MRI using dynamic Harmony Search based clustering 基于动态和谐搜索聚类的MRI骨肉瘤分割
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686624
M. Rajeswari, O. Alia, C. Bong, D. Ramachandram, M. Aziz, I. Shuaib
{"title":"Osteosarcoma segmentation in MRI using dynamic Harmony Search based clustering","authors":"M. Rajeswari, O. Alia, C. Bong, D. Ramachandram, M. Aziz, I. Shuaib","doi":"10.1109/SOCPAR.2010.5686624","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686624","url":null,"abstract":"In this paper, the automatic segmentation of Osteosar-coma in MRI images is formed as a clustering problem. Subsequently, a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means (FCM) called DCHS is proposed to automatically segment the Osteosarcoma MRI images in an intelligent manner. The concept of variable length in each harmony memory vector is applied to encode variable numbers of candidate cluster centers at each iteration. Furthermore, a new HS operator, called the 'empty operator' is introduced to support the selection of empty decision variables in the harmony memory vector. FCM is incorporated in DCHS to fine tune the segmentation results. Our approach uses multi-spectral information from STIR (Short Tau Inversion Recovery) and T2-weighted MRI sequences. We used a subset of Haralick texture features and pixel intensity values as a feature space to DCHS to delineate the tumour volume. The segmentation results were statistically evaluated against manually delineated data for four patients. Promising results were obtained with average of 0.72 of Dice measurement. In addition, we also propose a method to identify necrotic tissue within the tumour in order to monitor drug-induced necrosis of tumor tissue.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132543643","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: 18
Integration of geometric and dynamic features for facial expression recognition in color image sequences 彩色图像序列中面部表情识别的几何特征与动态特征集成
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686080
R. Niese, A. Al-Hamadi, B. Michaelis, H. Neumann
{"title":"Integration of geometric and dynamic features for facial expression recognition in color image sequences","authors":"R. Niese, A. Al-Hamadi, B. Michaelis, H. Neumann","doi":"10.1109/SOCPAR.2010.5686080","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686080","url":null,"abstract":"This work proposes a new approach for facial expression recognition in color image sequences, based on integrated evaluation of geometric and dynamic features. For this purpose a series of methods is introduced that on the one hand achieve high recognition rates for expressive facial behavior and on the other hand address a couple of common problems in this area of research. In particular we apply physiologically motivated image regions for the detection of dynamic features by using an optical flow method. In this way dynamic features capture the variations caused by facial expression changes. Opposed, geometric features do not contain temporal information but describe spatial feature parameters. These correspond to 3-D based Euclidean distances and angles. Particularly, the hypothesis of this work is that through integrated evaluation of geometric and dynamic features, improved recognition rates can be achieved. Based on comprehensive experimental investigations we show the advantage of the suggested approach.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129699161","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: 4
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