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

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Parallel AlineaGA: An island parallel evolutionary algorithm for multiple sequence alignment Parallel AlineaGA:一种多序列比对孤岛并行进化算法
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686492
F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez
{"title":"Parallel AlineaGA: An island parallel evolutionary algorithm for multiple sequence alignment","authors":"F. Silva, J. M. Sánchez-Pérez, J. Pulido, M. A. Vega-Rodríguez","doi":"10.1109/SOCPAR.2010.5686492","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686492","url":null,"abstract":"Multiple sequence alignment is the base of a growing number of Bioinformatics applications. This does not mean that the accuracy of the existing methods corresponds to biologically faultless alignments. Searching for the optimal alignment for a set of sequences is often hindered by the size and complexity of the search space. Parallel Genetic Algorithms are a class of stochastic algorithms which can increase the speed up of the algorithms. They also enhance the efficiency of the search and the robustness of the solutions by delivering results that are better than those provided by the sum of several sequential Genetic Algorithms. AlineaGA is an evolutionary method for solving protein multiple sequence alignment. It uses a Genetic Algorithm on which some of its genetic operators embed a simple local search optimization. We have implemented its parallel version which we now present. Comparing with its sequential version we have observed an improvement in the search for the best solution. We have also compared its performance with ClustalW2 and T-Coffee, observing that Parallel AlineaGA can lead the search for better solutions for the majority of the datasets in study.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"4 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":"126407701","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: 6
Using ants as a genetic crossover operator in GLS to solve STSP 利用蚁群作为遗传交叉算子在GLS中求解STSP
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686165
H. Ismkhan, K. Zamanifar
{"title":"Using ants as a genetic crossover operator in GLS to solve STSP","authors":"H. Ismkhan, K. Zamanifar","doi":"10.1109/SOCPAR.2010.5686165","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686165","url":null,"abstract":"Ant Colony Algorithm (ACA) and Genetic Local Search (GLS) are two optimization algorithms that have been successfully applied to the Traveling Salesman Problem (TSP). In this paper we define new crossover operator then redefine ACA's ants as operate according to defined crossover operator then put forward our GLS that uses these ants to solve Symmetric TSP (STSP) instances.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"123 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":"114423180","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: 9
Recognition of signed expressions using visually-Oriented subunits obtained by an immune-Based optimization 基于免疫优化的视觉导向亚基识别符号表达式
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1504/IJDMMM.2014.063198
Mariusz Oszust, M. Wysocki
{"title":"Recognition of signed expressions using visually-Oriented subunits obtained by an immune-Based optimization","authors":"Mariusz Oszust, M. Wysocki","doi":"10.1504/IJDMMM.2014.063198","DOIUrl":"https://doi.org/10.1504/IJDMMM.2014.063198","url":null,"abstract":"The paper considers automatic visual recognition of signed expressions. The proposed method is based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. To define the subunits a data-driven procedure is applied. The procedure consists in partitioning time series, extracted from video, into subsequences which form homogeneous groups. The cut points are determined by an immune optimization procedure based on quality assessment of the resulting clusters. In the paper the problem is formulated, its solution method is proposed and experimentally verified on a database of 100 Polish words. The results show that our subunit-based classifier outperforms its whole-word-based counterpart, which is particularly evident when new words are recognized on the basis of a small number of examples.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"36 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":"122567502","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
Horizontal format data mining with extended bitmaps 扩展位图的水平格式数据挖掘
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686156
BuddhikaDe Alwis, Supun Malinga, Kathiravelu Pradeeban, Denis Weerasiri, Shehan Perera
{"title":"Horizontal format data mining with extended bitmaps","authors":"BuddhikaDe Alwis, Supun Malinga, Kathiravelu Pradeeban, Denis Weerasiri, Shehan Perera","doi":"10.1109/SOCPAR.2010.5686156","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686156","url":null,"abstract":"Analysing the data warehouses to foresee the patterns of the transactions often needs high computational power and memory space due to the huge set of past history of the data transactions. Apriori algorithm is a mostly learned and implemented algorithm that mines the data warehouses to find the associations. Frequent item set mining with vertical data format has been proposed as an improvement over the basic Apriori algorithm. In this paper we are proposing an algorithm as an alternative to Apriori algorithm, which will use bitmap indices in conjunction with a horizontal format data set converted to a vertical format data structure to mine frequent itemsets leveraging efficiencies of bitmap based operations and vertical format data orientation.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"2015 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":"121447187","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
Learning spatial relationships in hand-drawn patterns using fuzzy mathematical morphology 使用模糊数学形态学学习手绘图案中的空间关系
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686091
Adrien Delaye, É. Anquetil
{"title":"Learning spatial relationships in hand-drawn patterns using fuzzy mathematical morphology","authors":"Adrien Delaye, É. Anquetil","doi":"10.1109/SOCPAR.2010.5686091","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686091","url":null,"abstract":"We introduce in this work a new approach for learning spatial relationships between elements of hand-drawn patterns with the help of fuzzy mathematical morphology operators. Relying on mathematical morphology allows to take into account the actual shapes of hand-drawn patterns when modeling their spatial relationships, and thus to cope with the variability of handwriting signal. Extension of mathematical morphology to the fuzzy set framework further allows to handle imprecision of handwriting and to deal with the ambiguity of spatial relationships. The novelty lies in the generative aspect of the models we propose, in the sense that they can exhibit the region of space where the learnt relation is satisfied with respect to a reference object, and can thus be used for driving structural analysis of complex patterns. Experiments over on-line handwritten data show their performance, and prove their ability to deal with variability of handwriting and reasoning under imprecision.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"126 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":"122961027","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
A self-growing Bayesian network classifier for online learning of human motion patterns 用于在线学习人体运动模式的自生长贝叶斯网络分类器
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686087
Zhuo Chen, Nelson H. C. Yung
{"title":"A self-growing Bayesian network classifier for online learning of human motion patterns","authors":"Zhuo Chen, Nelson H. C. Yung","doi":"10.1109/SOCPAR.2010.5686087","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686087","url":null,"abstract":"This paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are accordingly updated. As such, the proposed classifier allows current trajectories to form new HMP classes when they are sufficiently different from existing HMP classes. The performance of the proposed classifier is evaluated by a set of real-world data. The results show that the proposed classifier effectively learns new HMP classes from current trajectories in an online manner.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"14 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":"128853549","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
Prediction of the MSCI EURO index based on fuzzy grammar fragments extracted from European Central Bank statements 基于从欧洲央行声明中提取的模糊语法片段的MSCI欧元指数预测
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686083
V. Milea, N. Sharef, Rui Jorge Almeida, U. Kaymak, F. Frasincar
{"title":"Prediction of the MSCI EURO index based on fuzzy grammar fragments extracted from European Central Bank statements","authors":"V. Milea, N. Sharef, Rui Jorge Almeida, U. Kaymak, F. Frasincar","doi":"10.1109/SOCPAR.2010.5686083","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686083","url":null,"abstract":"We focus on predicting the movement of the MSCI EURO index based on European Central Bank (ECB) statements. For this purpose we learn and extract fuzzy grammars from the text of the ECB statements. Based on a set of selected General Inquirer (GI) categories, the extracted fuzzy grammars are grouped around individual content categories. The frequency at which these fuzzy grammars are encountered in the text constitute input to a Fuzzy Inference System (FIS). The FIS maps these frequencies to the levels of the MSCI EURO index. Ultimately, the goal is to predict whether the MSCI EURO index will exhibit upward or downward movement based on the content of ECB statements, as quantified through the use of fuzzy grammars and GI content categories.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"17 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":"129063230","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
Multivariate filter and PSO in protein function classification 多元滤波与粒子群算法在蛋白质功能分类中的应用
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5686158
S. A. Rahman, Z. Mohamed-Hussein, A. Bakar
{"title":"Multivariate filter and PSO in protein function classification","authors":"S. A. Rahman, Z. Mohamed-Hussein, A. Bakar","doi":"10.1109/SOCPAR.2010.5686158","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686158","url":null,"abstract":"Protein features are often complex, and they are challenging to classify. In identifying the most discriminatory features in protein sequences, we propose a new feature-selection strategy by integrating the multivariate filter and Particle Swarm Optimisation (PSO) algorithms. Experimental results, based on the number of reducts and classification accuracy, were analysed in both the filter and wrapper phases. For our dataset, the proposed method statistically significantly improves the obtained classification accuracy and reduces the number of feature subsets. In the filter phase, the accuracy is improved more than 4% in three out of four multivariate feature selection methods compared to a model without feature selection. In the second phase, the accuracy is increased from 97.51% to 100%. We also demonstrate the importance of the correct parameter settings in the PSO to guarantee good performance.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"37 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":"116024612","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: 1
Pheromone trail initialization with local optimal solutions in ant colony optimization 蚁群优化中信息素轨迹初始化的局部最优解
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.3233/KES-130282
H. Kanoh, J. Ochiai, Yosuke Kameda
{"title":"Pheromone trail initialization with local optimal solutions in ant colony optimization","authors":"H. Kanoh, J. Ochiai, Yosuke Kameda","doi":"10.3233/KES-130282","DOIUrl":"https://doi.org/10.3233/KES-130282","url":null,"abstract":"This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal solutions calculated in advance. Max-Min Ant System has demonstrated impressive performance, but the rate of search is relatively low. Considering the generic purpose of stochastic search algorithms, which is to find near optimal solutions subject to time constraints, the rate of search is important as well as the quality of the solution. The experimental results using benchmark problems with 51 to 318 cities suggested that the proposed method is better than the conventional method in both the quality of the solution and the rate of search.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"29 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":"126940437","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
Fuzzy methods for forensic data analysis 法医数据分析的模糊方法
2010 International Conference of Soft Computing and Pattern Recognition Pub Date : 2010-12-01 DOI: 10.1109/SOCPAR.2010.5685848
K. Stoffel, Paul Cotofrei, Dong Han
{"title":"Fuzzy methods for forensic data analysis","authors":"K. Stoffel, Paul Cotofrei, Dong Han","doi":"10.1109/SOCPAR.2010.5685848","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685848","url":null,"abstract":"In this paper we describe a methodology and an automatic procedure for inferring accurate and easily understandable expert-system-like rules from forensic data. This methodology is based on the fuzzy set theory. The algorithms we used are described in detail, and were tested on forensic data sets. We also present in detail some examples, which are representative for the obtained results.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"50 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":"125171187","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: 53
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