2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)最新文献_第2页

Ensemble classification of PolSAR data using multi-objective heuristic combination rule 基于多目标启发式组合规则的PolSAR数据集成分类

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482132

R. Saleh, H. Farsi, Seyyed Hamid Zahiri

引用次数: 2

Training spiking neurons with gravitational search algorithm for data classification 用重力搜索算法训练尖峰神经元进行数据分类

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482125

M. B. Dowlatshahi, M. Rezaeian

{"title":"Training spiking neurons with gravitational search algorithm for data classification","authors":"M. B. Dowlatshahi, M. Rezaeian","doi":"10.1109/CSIEC.2016.7482125","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482125","url":null,"abstract":"Rather than producing a reaction in its output each iteration, as traditional neurons work, a spiking neuron is excited T ms with an input and actives when a particular value for membrane potential of it obtained. This reaction could possibly be converted to a special firing rate and do a data classification problem based on the firing rate produced by the input signal. Given a set of input instances each belongs to one of the K classes, in this case each input instance is mapped into an input current, then the spiking neuron is excited T ms, and finally the firing rate of input instance is calculated. This model is validated based on next property: data belong to the similar class must produce the same firing rates and data belong to other classes need to produce firing rates adequately different to differentiate among the classes. To provide this property, a training stage id needed to optimize the synaptic weights of model. Gravitational Search Algorithm (GSA) is a novel optimization algorithm designed for solving complex optimization problems. This algorithm has a very much adjusted system for balancing between exploration and exploitation. In this paper, we optimize the synaptic weights of a spiking neuron by GSA. The performance of the proposed algorithm is assessed through four standard benchmark datasets from the UCI Machine Learning Repository. The performance of proposed GSA is compared against the results reported for the same spiking neuron trained with the Differential Evolution (DE) algorithm, the Particle Swarm Optimization (PSO) algorithm, and the Cuckoo Search (CS) algorithm.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116350577","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

Using memetic algorithms for test case prioritization in model based software testing 模因算法在基于模型的软件测试中的应用

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482129

Fatemeh Mosala Nejad, R. Akbari, Mohammad Mehdi Dejam

引用次数: 9

A PSO-based weighting method to enhance machine learning techniques for cooperative spectrum sensing in CR networks 基于pso的加权方法增强CR网络中协同频谱感知的机器学习技术

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482127

Elham Ghazizadeh, Bahareh Nikpour, D. A. Moghadam, H. Nezamabadi-pour

{"title":"A PSO-based weighting method to enhance machine learning techniques for cooperative spectrum sensing in CR networks","authors":"Elham Ghazizadeh, Bahareh Nikpour, D. A. Moghadam, H. Nezamabadi-pour","doi":"10.1109/CSIEC.2016.7482127","DOIUrl":"https://doi.org/10.1109/CSIEC.2016.7482127","url":null,"abstract":"Cognitive radio (CR) is a recent technology to tackle the problem of radio spectrum scarcity. Successful spectrum sensing is fundamental in performance of CR networks; hence, a PSO-based weighting method is proposed in order to improve the functionality of machine learning techniques which are used with the aim of detecting the activity of secondary users in cooperative cognitive radio (CCR) networks. Regarding classification methods, three supervised classifiers which are supported vector machines (SVM), K-nearest neighbors (K-NN) and naïve Bayes are used for pattern classification. Since our goal is spectrum sensing in CCR networks, the vector of energy levels in radio channel which is considered as a feature vector is fed into the classifier to determine the availability of the channel. The classifier labels each feature vector as two classes: the \"channel available class\" or the \"channel unavailable class\". In our proposed method, first, the three mentioned classifiers go through a training phase. Next, for new feature vectors, a label is assigned to the feature vector by each classifier and the final decision about the availability of the channel is made by a weighted voting method based on the PSO algorithm in an online fashion. The performance of our technique is measured in terms of the classification error. Also, the comparative results show twofold merit over previous methods since it not only reduces the error rate but also decreases the error of the channel available class.","PeriodicalId":268101,"journal":{"name":"2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"2 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133364661","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: 13

Sunshine: A novel random search for continuous global optimization Sunshine:一种新颖的连续全局优化随机搜索

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482111

Mohammadreza Jahedbozorgan, R. Amjadifard

引用次数: 2

Optimization of software cost estimation using harmony search algorithm 基于和谐搜索算法的软件成本估算优化

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482119

S. M. Sabbagh Jafari, F. Ziaaddini

引用次数: 14

Exploring methods and systems for vision based human activity recognition 探索基于视觉的人类活动识别方法和系统

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482122

Eisa Jafari Amirbandi, G. Shamsipour

引用次数: 6

A parallel grey wolf optimizer combined with opposition based learning 结合对立学习的并行灰狼优化器

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482116

Mohammad Sohrabi Nasrabadi, Y. Sharafi, Mohammad Tayari

引用次数: 20

Increasing the efficiency of the Persian Sign Language system based on new preprocessing method 基于新的预处理方法提高波斯语手语系统的效率

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482118

Leila Yavari, Hosein Sadati, S. Mozaffari

引用次数: 0

SubLex: Generating subjectivity lexicons using genetic algorithm for subjectivity classification of big social data SubLex:利用遗传算法生成主体性词汇，用于大社会数据的主体性分类

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) Pub Date : 2016-03-09 DOI: 10.1109/CSIEC.2016.7482126

Hamidreza Keshavarz, M. S. Abadeh

引用次数: 8