2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)最新文献

Genetic algorithm-based neural error correcting output classifier 基于遗传算法的神经纠错输出分类器

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015745

Mahdi Amina, F. Masulli, S. Rovetta

引用次数: 2

TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm 基于目标函数的模糊聚类算法辨识TS模糊模型

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015742

T. Dam, A. K. Deb

引用次数: 6

Hyper-heuristic approach for solving nurse rostering problem 解决护士名册问题的超启发式方法

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015743

K. Anwar, M. Awadallah, A. Khader, M. Al-Betar

引用次数: 11

Empirical mode decomposition based adaboost-backpropagation neural network method for wind speed forecasting 基于经验模态分解的adaboost-反向传播神经网络风速预报方法

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015741

Ye Ren, Xueheng Qiu, P. N. Suganthan

引用次数: 14

Ensemble based classification using small training sets : A novel approach 基于小训练集的集成分类:一种新方法

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015738

C. V. K. Veni, Timmappareddy Sobha Rani

{"title":"Ensemble based classification using small training sets : A novel approach","authors":"C. V. K. Veni, Timmappareddy Sobha Rani","doi":"10.1109/CIEL.2014.7015738","DOIUrl":"https://doi.org/10.1109/CIEL.2014.7015738","url":null,"abstract":"Classification is a supervised learning technique typically uses two-thirds of the given annotated data set for training and the remaining for test. In this paper, we developed a frame work which uses less than one-third of the data set for training and tests the remaining two-thirds of the data and still gives results comparable to other classifiers. To achieve good classification accuracy with small training sets, we focused on three issues: The first is that, one-third(30%) of the data should represent the entire data set. The second is on increasing the classification accuracy even with these small training sets, and the third issue is on taking care of deviations in the small training sets like noise or outliers. First issue is addressed by proposing three methods: divide the instances into 10 bins based on their distances from the centroid, based on their distance from a reference point 3/2(min+max) and a distribution specific binning. In all these methods, training sets are formed using stratified sampling approach which ensures that the samples chosen are from the entire distribution. Second issue is dealt with using the concept of ensemble based weighted majority voting for classification. Third issue is tackled by implementing four filters on training sets. The filters used are Removing Outliers using Inter Quartile Range option (available in Weka) and removing misclassified instances applying Naive Bayes, IB3, IB5 as filters. Experiments are conducted on seven binary andmulti-class data sets taking only 6% to 18% of the total data for training and implemented the proposed three methods without any filters for noise and outlier removal and with them too on the training sets.We compare our results with two popular ensemble methods ada-boost and bagging ensemble techniques, ENN, CNN, RNN instance selection methods. Empirical analysis shows that our three proposed methods yield comparable classification results to those available in literature which use small training sets.","PeriodicalId":229765,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123998633","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

Building predictive models in two stages with meta-learning templates optimized by genetic programming 利用遗传规划优化的元学习模板分两阶段构建预测模型

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015740

P. Kordík, J. Cerný

{"title":"Building predictive models in two stages with meta-learning templates optimized by genetic programming","authors":"P. Kordík, J. Cerný","doi":"10.1109/CIEL.2014.7015740","DOIUrl":"https://doi.org/10.1109/CIEL.2014.7015740","url":null,"abstract":"The model selection stage is one of the most difficult in predictive modeling. To select a model with a highest generalization performance involves benchmarking huge number of candidate models or algorithms. Often, a final model is selected without considering potentially high quality candidates just because there are too many possibilities. Improper benchmarking methodology often leads to biased estimates of model generalization performance. Automation of the model selection stage is possible, however the computational complexity is huge especially when ensembles of models and optimization of input features should be also considered. In this paper we show, how to automate model selection process in a way that allows to search for complex hierarchies of ensemble models while maintaining computational tractability. We introduce two-stage learning, meta-learning templates optimized by evolutionary programming with anytime properties to be able to deliver and maintain data-tailored algorithms and models in a reasonable time without human interaction. Co-evolution if inputs together with optimization of templates enabled to solve algorithm selection problem efficiently for variety of datasets.","PeriodicalId":229765,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117106091","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

The entity-to-algorithm allocation problem: extending the analysis 实体-算法分配问题:扩展分析

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015744

J. Grobler, A. Engelbrecht, G. Kendall, V. Yadavalli

引用次数: 4

Fast image segmentation based on boosted random forests, integral images, and features on demand 基于增强随机森林、积分图像和按需特征的快速图像分割

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015737

U. Knauer, U. Seiffert

{"title":"Fast image segmentation based on boosted random forests, integral images, and features on demand","authors":"U. Knauer, U. Seiffert","doi":"10.1109/CIEL.2014.7015737","DOIUrl":"https://doi.org/10.1109/CIEL.2014.7015737","url":null,"abstract":"The paper addresses the tradeoff between speed and quality of image segmentation typically found in real-time or high-throughput image analysis tasks. We propose a novel approach for high-quality image segmentation based on a rich and high-dimensional feature space and strong classifiers. To enable fast feature extraction in color images, multiple integral images are used. A decision tree based approach based on twostage Random Forest classifiers is utilized to solve several binary as well as multiclass segmentation problems. It is an intrinsic property of the tree based approach, that any decision is based on a small subset of input features only. Hence, analysis of the tree structures enables a sequential feature extraction. Runtime measurements with several real-world datasets show that the approach enables fast high-quality segmentation. Moreover, the approach can be easily used in parallel computation frameworks because calculation of integral images as well as computation of individual decisions can be done separately. Also, the number of base classifiers can be easily adapted to meet a certain time constraint.","PeriodicalId":229765,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129234082","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

Experiments on simultaneous combination rule training and ensemble pruning algorithm 组合规则训练与集成剪枝算法的同步实验

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-12-01 DOI: 10.1109/CIEL.2014.7015736

B. Krawczyk, Michal Wozniak

{"title":"Experiments on simultaneous combination rule training and ensemble pruning algorithm","authors":"B. Krawczyk, Michal Wozniak","doi":"10.1109/CIEL.2014.7015736","DOIUrl":"https://doi.org/10.1109/CIEL.2014.7015736","url":null,"abstract":"Nowadays many researches related to classifier design are trying to exploit strength of the ensemble learning. Such hybrid approach looks for the valuable combination of individual classifiers' outputs, which should at least outperforms quality of the each available individuals. Therefore the classifier ensembles are recently the focus of intense research. Basically, it faces with two main problems. On the one hand we look for the valuable, highly diverse pool of individual classifiers, i.e., they are expected to be mutually complimentary. On the other hand we try to propose an optimal combination of the individuals' outputs. Usually, mentioned above tasks are considering independently, i.e., there are several approaches which focus on the ensemble pruning only for a given combination rule, while the others works are devoted to the problem how to find an optimal combination rule for a fixed line-up of classifier pool. In this work we propose to put ensemble pruning and combination rule training together and consider them as the one optimization task. We employ a canonical genetic algorithm to find the best ensemble line-up and in the same time the best set-up of the combination rule parameters. The proposed concept (called CRUMP - simultaneous Combination RUle training and enseMble Pruning) was evaluated on the basis the wide range of computer experiments, which confirmed that this is the very promising direction which is able to outperform the traditional approaches focused on either the ensemble pruning or combination rule.","PeriodicalId":229765,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359130","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

Ensemble deep learning for regression and time series forecasting 用于回归和时间序列预测的集成深度学习

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) Pub Date : 2014-01-20 DOI: 10.1109/CIEL.2014.7015739

Xueheng Qiu, Le Zhang, Ye Ren, P. N. Suganthan, G. Amaratunga

引用次数: 310