2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第9页

Railway platform reallocation after dynamic perturbations using ant colony optimisation 基于蚁群优化的动态扰动后铁路站台再分配

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849965

Jayne Eaton, Shengxiang Yang

引用次数: 2

An ensemble of single multiplicative neuron models for probabilistic prediction 用于概率预测的单个乘法神经元模型的集合

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849975

U. Yolcu, Yaochu Jin, E. Eğrioğlu

{"title":"An ensemble of single multiplicative neuron models for probabilistic prediction","authors":"U. Yolcu, Yaochu Jin, E. Eğrioğlu","doi":"10.1109/SSCI.2016.7849975","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849975","url":null,"abstract":"Inference systems basically aim to provide and present the knowledge (outputs) that decision-makers can take advantage of in their decision-making process. Nowadays one of the most commonly used inference systems for time series prediction is the computational inference system based on artificial neural networks. Although they have the ability of handling uncertainties and are capable of solving real life problems, neural networks have interpretability issues with regard to their outputs. For example, the outputs of neural networks that are difficult to interpret compared to statistical inference systems' outputs that involve a confidence interval and probabilities about possible values of predictions on top of the point estimations. In this study, an ensemble of single multiplicative neuron models based on bootstrap technique has been proposed to get probabilistic predictions. The main difference of the proposed ensemble model compared to conventional neural network models is that it is capable of getting a bootstrap confidence interval and probabilities of predictions. The performance of the proposed model is demonstrated on different time series. The obtained results show that the proposed ensemble model has a superior prediction performance in addition to having outputs that are more interpretable and applicable to probabilistic evaluations than conventional neural networks.","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":"130443887","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: 8

Adapting linear discriminant analysis to the paradigm of learning from label proportions 将线性判别分析应用于标签比例学习范式

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850150

M. Pérez-Ortiz, Pedro Antonio Gutiérrez, Mariano Carbonero-Ruz, C. Hervás‐Martínez

{"title":"Adapting linear discriminant analysis to the paradigm of learning from label proportions","authors":"M. Pérez-Ortiz, Pedro Antonio Gutiérrez, Mariano Carbonero-Ruz, C. Hervás‐Martínez","doi":"10.1109/SSCI.2016.7850150","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850150","url":null,"abstract":"The recently coined term “learning from label proportions” refers to a new learning paradigm where training data is given by groups (also denoted as “bags”), and the only known information is the label proportion of each bag. The aim is then to construct a classification model to predict the class label of an individual instance, which differentiates this paradigm from the one of multi-instance learning. This learning setting presents very different applications in political science, marketing, healthcare and, in general, all fields in relation with anonymous data. In this paper, two new strategies are proposed to tackle this kind of problems. Both proposals are based on the optimisation of pattern class memberships using the data distribution in each bag and the known label proportions. To do so, linear discriminant analysis has been reformulated to work with non-crisp class memberships. The experimental part of this paper sets different objetives: 1) study the difference in performance, comparing our proposals and the fully supervised setting, 2) analyse the potential benefits of refining class memberships by the proposed approaches, and 3) test the influence of other factors in the performance, such as the number of classes or the bag size. The results of these experiments are promising, but further research should be encouraged for studying more complex data configurations.","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":"129288545","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

SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies SIMARD:一种具有质量预选策略的基于模拟退火的RNA设计算法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849957

Sinem Sav, David J. D. Hampson, Herbert H. Tsang

{"title":"SIMARD: A simulated annealing based RNA design algorithm with quality pre-selection strategies","authors":"Sinem Sav, David J. D. Hampson, Herbert H. Tsang","doi":"10.1109/SSCI.2016.7849957","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849957","url":null,"abstract":"Most of the biological processes including expression levels of genes and translation of DNA to produce proteins within cells depend on RNA sequences, and the structure of the RNA plays vital role for its function. RNA design problem refers to the design of an RNA sequence that folds into given secondary structure. However, vast number of possible nucleotide combinations make this an NP-Hard problem. To solve the RNA design problem, a number of researchers have tried to implement algorithms using local stochastic search, context-free grammars, global sampling or evolutionary programming approaches. In this paper, we examine SIMARD, an RNA design algorithm that implements simulated annealing techniques. We also propose QPS, a mutation operator for SIMARD that pre-selects high quality sequences. Furthermore, we present experiment results of SIMARD compared to eight other RNA design algorithms using the Rfam datset. The experiment results indicate that SIMARD shows promising results in terms of Hamming distance between designed sequence and the target structure, and outperforms ERD in terms of free energy.","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":"128823655","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

A new approach to session identification by applying fuzzy c-means clustering on web logs 基于模糊c均值聚类的web日志会话识别新方法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849939

D. Koutsoukos, Georgios Alexandridis, Georgios Siolas, A. Stafylopatis

引用次数: 7

A direct memetic approach to the solution of Multi-Objective Optimal Control Problems 多目标最优控制问题解的直接模因法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850103

M. Vasile, Lorenzo A. Ricciardi

{"title":"A direct memetic approach to the solution of Multi-Objective Optimal Control Problems","authors":"M. Vasile, Lorenzo A. Ricciardi","doi":"10.1109/SSCI.2016.7850103","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850103","url":null,"abstract":"This paper proposes a memetic direct transcription algorithm to solve Multi-Objective Optimal Control Problems (MOOCP). The MOOCP is first transcribed into a Non-linear Programming Problem (NLP) with Direct Finite Elements in Time (DFET) and then solved with a particular formulation of the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents combines local search heuristics, exploring the neighbourhood of each agent, with social actions exchanging information among agents. A collection of all Pareto optimal solutions is maintained in an archive that evolves towards the Pareto set. In the approach proposed in this paper, individualistic actions run a local search, from random points within the neighbourhood of each agent, solving a normalised Pascoletti-Serafini scalarisation of the multi-objective NLP problem. Social actions, instead, solve a bi-level problem in which the lower level handles only the constraint equations while the upper level handles only the objective functions. The proposed approach is tested on the multi-objective extensions of two well-known optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem.","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":"121474727","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: 8

A new fast large neighbourhood search for service network design with asset balance constraints 资产平衡约束下服务网络设计的快速大邻域搜索

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850084

Ruibin Bai, J. Woodward, N. Subramanian

引用次数: 1

Mapping spatio-temporally encoded patterns by reward-modulated STDP in Spiking neurons 通过奖励调制的STDP在尖峰神经元中映射时空编码模式

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850248

Ibrahim Ozturk, D. Halliday

引用次数: 2

Region-of-interest extraction of fMRI data using genetic algorithms 利用遗传算法提取功能磁共振成像数据的兴趣区域

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850135

S. Hiwa, Yuuki Kohri, Keisuke Hachisuka, T. Hiroyasu

{"title":"Region-of-interest extraction of fMRI data using genetic algorithms","authors":"S. Hiwa, Yuuki Kohri, Keisuke Hachisuka, T. Hiroyasu","doi":"10.1109/SSCI.2016.7850135","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850135","url":null,"abstract":"Functional connectivity, which is indicated by time-course correlations of brain activities among different brain regions, is one of the most useful metrics to represent human brain states. In functional connectivity analysis (FCA), the whole brain is parcellated into a certain number of regions based on anatomical atlases, and the mean time series of brain activities are calculated. Then, the correlation between mean signals of two regions is repeatedly calculated for all combinations of regions, and finally, we obtain the correlation matrix of the whole brain. FCA allows us to understand which regions activate cooperatively during specific stimulus or tasks. In this study, we attempt to represent human brain states using functional connectivity as feature vectors. As there are a number of brain regions, it is difficult to determine which regions are prominent to represent the brain state. Therefore, we proposed an automatic region-of-interest (ROI) extraction method to classify human brain states. Time-series brain activities were measured by functional magnetic resonance imaging (fMRI), and FCA was performed. Each element of the correlation matrix was used as a feature vector for brain state classification, and element characteristics were learned using supervised learning methods. The elements used as feature vectors, i.e., ROIs, were determined automatically using a genetic algorithm to maximize the classification accuracy of brain states. fMRI data measured during two emotional conditions, i.e., pleasant and unpleasant emotions, were used to show the effectiveness of the proposed method. Numerical experiments revealed that the proposed method could extract the superior frontal gyrus, orbitofrontal cortex, cuneus, cerebellum, and cerebellar vermis as ROIs associated with pleasant and unpleasant emotions.","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":"125709756","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: 5

Advanced parallel copula based EDA 基于并行耦合的高级EDA

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850202

Martin Hyrs, J. Schwarz

引用次数: 0