2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第8页

Automated classification for pathological prostate images using AdaBoost-based Ensemble Learning 基于adaboost的集成学习的病理前列腺图像自动分类

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849887

Chao-Hui Huang, E. Kalaw

引用次数: 5

An incremental learning mechanism for human activity recognition 人类活动识别的增量学习机制

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850188

S. Ntalampiras, M. Roveri

引用次数: 13

Dimension reduction in classification using particle swarm optimisation and statistical variable grouping information 基于粒子群优化和统计变量分组信息的分类降维

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850126

Bing Xue, M. C. Lane, Ivy Liu, Mengjie Zhang

{"title":"Dimension reduction in classification using particle swarm optimisation and statistical variable grouping information","authors":"Bing Xue, M. C. Lane, Ivy Liu, Mengjie Zhang","doi":"10.1109/SSCI.2016.7850126","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850126","url":null,"abstract":"Dimension reduction is a preprocessing step in many classification tasks, but reducing dimensionality and finding the optimal set of features or attributes are challenging because of the big search space and interactions between attributes. This paper proposes a new dimension reduction method by using a statistical variable grouping method that groups similar attributes into a group by considering interaction between attributes and using particle swarm optimisation as a search technique to adopt the discovered statistical grouping information to search optimal attribute subsets. Two types of approaches are developed, where the first aims to select one attribute from each group to reduce the dimensionality, and the second allows the selection of multiple attributes from one group to further improve the classification performance. Experiments on ten datasets of varying difficulties show that all the two approaches can successfully address dimension reduction tasks to decrease the number of attributes, and achieve the similar of better classification performance. The first approach selects a smaller number of attributes than the second approach while the second approach achieves better classification performance. The proposed new algorithms outperform other recent dimension reduction algorithms in terms of the classification performance, or further reduce the number of attributes while maintaining the classification performance.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128710969","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

Estimating force mix lower bounds using a multi-objective evolutionary algorithm 用多目标进化算法估计力混合下界

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850071

Fred Ma, S. Wesolkowski

引用次数: 1

The use of Kernel PCA in evolutionary optimization for computationally demanding engineering applications 核主成分分析在进化优化中对计算要求高的工程应用的应用

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850203

D. Kapsoulis, K. Tsiakas, V. Asouti, K. Giannakoglou

{"title":"The use of Kernel PCA in evolutionary optimization for computationally demanding engineering applications","authors":"D. Kapsoulis, K. Tsiakas, V. Asouti, K. Giannakoglou","doi":"10.1109/SSCI.2016.7850203","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850203","url":null,"abstract":"Two techniques to further enhance the efficiency of Evolutionary Algorithms (EAs), even those which have already been accelerated by implementing surrogate evaluation models or metamodels to overcome a great amount of costly evaluations, are presented. Both rely upon the use of a Kernel Principal Component Analysis (Kernel PCA or KPCA) of the design space, as this reflects upon the offspring population in each generation. The PCA determines a feature space where the evolution operators should preferably be applied. In addition, in Metamodel-Assisted EA (MAEAs), the PCA can reduce the number of sensory units of metamodels. Due to the latter, the metamodels yield better approximations to the objective function value. This paper extends previous work by the authors which was based on Linear PCA, used for the same purposes. In the present paper, the superiority of using the Kernel (rather than the Linear) PCA, especially in real-world applications, is demonstrated. The proposed methods are assessed in single- and two-objective mathematical optimization problems and, finally, showcased in aerodynamic shape optimization problems with computationally expensive evaluation software.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127186492","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: 11

Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments 极端有效波高段预测的杂交神经网络模型

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850144

A. M. Durán-Rosal, J. C. Fernández, Pedro Antonio Gutiérrez, C. Hervás‐Martínez

引用次数: 2

Evaluating Fuzzy Analogy on incomplete software projects data 不完全软件项目数据的模糊类比评价

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849922

Ibtissam Abnane, A. Idri

{"title":"Evaluating Fuzzy Analogy on incomplete software projects data","authors":"Ibtissam Abnane, A. Idri","doi":"10.1109/SSCI.2016.7849922","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849922","url":null,"abstract":"Missing Data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort prediction systems. This paper investigates the use of missing data (MD) techniques with Fuzzy Analogy. More specifically, this study analyze the predictive performance of this analogy-based technique when using toleration, deletion or k-nearest neighbors (KNN) imputation techniques using the Pred(0.25) accuracy criterion and thereafter compares the results with the findings when using the Standardized Accuracy (SA) measure. A total of 756 experiments were conducted involving seven data sets, three MD techniques (toleration, deletion and KNN imputation), three missingness mechanisms (MCAR: missing completely at random, MAR: missing at random, NIM: non-ignorable missing), and MD percentages from 10 percent to 90 percent. The results of accuracy measured in terms of Pred(0.25) confirm the findings of a study which used the SA measure. Moreover, we found that SA and Pred(0.25) measure different aspects of technique performance. Hence, SA is not sufficient to conclude about the technique accuracy and it should be used with other metrics, especially Pred(0.25).","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130160192","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

Principled Evolutionary Algorithm search operator design and the kernel trick 原理进化算法的搜索算子设计和核技巧

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850204

Fergal Lane, R. Muhammad, Atif Azad, C. Ryan, Ireland Email, Fergal Lane, Ie

引用次数: 2

Evaluating the effectiveness of Bayesian and Neural Networks for Adaptive Schedulling Systems 评价贝叶斯和神经网络在自适应调度系统中的有效性

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849997

Bruno Cunha, A. Madureira, J. Pereira, I. Pereira

引用次数: 4

The emergency response management based on Bayesian decision network 基于贝叶斯决策网络的应急响应管理

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849973

Jiangnan Qiu, Wenjing Gu, Q. Kong, Qiuyan Zhong, Jilei Hu

引用次数: 6