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2010 UK Workshop on Computational Intelligence (UKCI)最新文献

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Feature selection and classification in bioscience/medical datasets: study of parameters and multi-objective approach in Two-Phase EA/k-NN method 生物科学/医学数据集的特征选择与分类:两阶段EA/k-NN方法的参数和多目标方法研究
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625581
M. Dissanayake, D. Corne
{"title":"Feature selection and classification in bioscience/medical datasets: study of parameters and multi-objective approach in Two-Phase EA/k-NN method","authors":"M. Dissanayake, D. Corne","doi":"10.1109/UKCI.2010.5625581","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625581","url":null,"abstract":"Feature selection continues to grow in importance in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such datasets, tools are required that can reliably distinguish the most relevant features. The latter is a useful goal in itself (e.g. such features may be putative drug targets), and also improves (perhaps drastically) both the speed of machine learning algorithms on the dataset, and the quality of predictive models. Among much research in feature selection methods, previous work has shown promise for an evolutionary algorithm/classifier combination (EA/k-NN), which, in successive phases of the same algorithm, serves first as the feature selection mechanism and second as the machine learning method yielding an accurate classifier. Here, we follow up that work by investigating the configuration and parametrisation of the two phases, including an investigation of multi-objective approaches for one or both phases. Following tests on three datasets, we find: further evidence that the two-phase approach is effective, with results on the most difficult dataset highly competitive with the literature; inconclusive results concerning the ideal way to configure the two phases; evidence in support of using a multi-objective approach in one or both phases.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"26 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120806594","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: 2
The importance of a piece difference feature to Blondie24 对Blondie24来说，单件差异的重要性
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625582
Belal Al-Khateeb, G. Kendall
{"title":"The importance of a piece difference feature to Blondie24","authors":"Belal Al-Khateeb, G. Kendall","doi":"10.1109/UKCI.2010.5625582","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625582","url":null,"abstract":"In recent years, significant research attention has been paid to evolving self-learning checkers players. Fogel's Blondie24 has been very successful in this field and has inspired other researchers to further develop this area. In this paper we address the question of whether piece difference is an important factor in the Blondie24 architecture. Although this issue has been addressed before, this work provides a different experimental setup to previous work, but arrives at the same conclusion. Our experiments show that piece difference has a significant effect on learning abilities.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115627987","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
Evolving the structure of Hidden Markov models for micro aneurysms detection 微动脉瘤检测中隐马尔可夫模型结构的演化
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625579
J. Goh, Lilian Tang, L. A. Al Turk
{"title":"Evolving the structure of Hidden Markov models for micro aneurysms detection","authors":"J. Goh, Lilian Tang, L. A. Al Turk","doi":"10.1109/UKCI.2010.5625579","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625579","url":null,"abstract":"Micro aneurysms are one of the first visible clinical signs of diabetic retinopathy and their detection can help diagnose the progression of the disease. In this paper, a novel technique based on Genetic Algorithms is used to evolve the structure of the Hidden Markov Models to obtain an optimised model that indicates the presence of micro aneurysms located in a sub-region. This technique not only identifies the optimal number of states, but also determines the topology of the Hidden Markov Model, along with the initial model parameters.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131618253","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
Engene: A genetic algorithm classifier for content-based recommender systems that does not require continuous user feedback 基因:基于内容的推荐系统的遗传算法分类器，不需要持续的用户反馈
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625594
J. Pagonis, A. Clark
{"title":"Engene: A genetic algorithm classifier for content-based recommender systems that does not require continuous user feedback","authors":"J. Pagonis, A. Clark","doi":"10.1109/UKCI.2010.5625594","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625594","url":null,"abstract":"We present Engene, a genetic algorithm based classifier which is designed for use in content-based recommender systems. Once bootstrapped Engene does not need any human feedback. Although it is primarily used as an on-line classifier, in this paper we present its use as a one-class document batch classifier and compare its performance against that of a one-class k-NN classifier.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123780236","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: 7
Mind the (computational) gap 注意(计算)差距
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625604
Matthew C. Casey, Athanasios Pavlou, A. Timotheou
{"title":"Mind the (computational) gap","authors":"Matthew C. Casey, Athanasios Pavlou, A. Timotheou","doi":"10.1109/UKCI.2010.5625604","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625604","url":null,"abstract":"Despite many advances in both computational intelligence and computational neuroscience, it is clear that we have yet to achieve the full potential of nature inspired solutions from studying the human brain. Models of brain function have reached the stage where large-scale models of the brain have become possible, yet these tantalising computational structures cannot yet be applied to real-world problems because they lack the ability to be connected to real-world inputs or outputs. This paper introduces the notion of creating a computational hub that has the potential to link real sensory stimuli to higher cortical models. This is achieved through modelling subcortical structures, such as the superior colliculus, which have desirable computational principles, including rapid, multisensory and discriminative processing. We demonstrate some of these subcortical principles in a system that performs real-time speaker localisation using live video and audio, showing how such models may help us bridge the computational gap.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130673729","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
Ant colony optimization with direct communication for the traveling salesman problem 旅行商问题的直接通信蚁群优化
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625608
Michalis Mavrovouniotis, Shengxiang Yang
{"title":"Ant colony optimization with direct communication for the traveling salesman problem","authors":"Michalis Mavrovouniotis, Shengxiang Yang","doi":"10.1109/UKCI.2010.5625608","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625608","url":null,"abstract":"Ants in conventional ant colony optimization (ACO) algorithms use pheromone to communicate. Usually, this indirect communication leads the algorithm to a stagnation behaviour, where the ants follow the same path from early stages. This occurs because high levels of pheromone are developed, which force the ants to follow the same corresponding trails. As a result, the population gets trapped into a local optimum solution which is difficult to escape from it. In this paper, a direct communication (DC) scheme is proposed where ants are able to exchange cities with other ants that belong to their communication range. Experiments show that the DC scheme delays convergence and improves the solution quality of conventional ACO algorithms regarding the traveling salesman problem, since it guides the population towards the global optimum solution. The ACO algorithm with the proposed DC scheme has better performance, especially on large problem instances, even though it increases the computational time in comparison with a conventional ACO algorithm.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117243618","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: 7
Equity markets and computational intelligence 股票市场和计算智能
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625605
Russ Abbott
{"title":"Equity markets and computational intelligence","authors":"Russ Abbott","doi":"10.1109/UKCI.2010.5625605","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625605","url":null,"abstract":"I propose a new characterization of the types of problems for which computational intelligence (CI) tends to be used, namely the identification of approximate abstractions. I then suggest that equity markets provide a challenging example for CI. Because markets are inherently adaptive, they pose a more difficult problem than traditional CI domains. I discuss my experience teaching a CI class that took the development of stock trading systems as a theme. A simple genetic algorithm to generate a trading strategy was developed as a class example. Although the astonishingly good results it achieved were due at least in part to data snooping, a simple unevolved version of the same strategy was almost as profitable. Yet it too had subtle data snooping problems—showing how difficult it is to avoid data snooping entirely, especially in adaptive domains.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130996354","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: 7
Uncertainty and type-2 fuzzy sets and systems 不确定性和2型模糊集和系统
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625603
Christian Wagner, H. Hagras
{"title":"Uncertainty and type-2 fuzzy sets and systems","authors":"Christian Wagner, H. Hagras","doi":"10.1109/UKCI.2010.5625603","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625603","url":null,"abstract":"As part of this paper we are highlighting several - in our opinion- important aspects of type-2 fuzzy logic systems which seem important to its future development and application. It is the aim of the paper to provide more questions and more suggestive points than actual answers. With type-2 fuzzy logic and its application to modelling and handling uncertainty still a very young area of research, most answers are bound yet to be discovered.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131127256","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: 21
Using genetic algorithms in word-vector optimisation 遗传算法在词向量优化中的应用
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625589
P. Smith
{"title":"Using genetic algorithms in word-vector optimisation","authors":"P. Smith","doi":"10.1109/UKCI.2010.5625589","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625589","url":null,"abstract":"Word vectors and sets of words are used in a wide range of text-based applications. Yet these word sets are often chosen on an ad hoc basis. In this study, we examine two text-based applications that use word sets and in both cases find that classification performance can be optimised using a fairly simple genetic algorithm. The first study is in authorship attribution, the second one is sentiment analysis and in both cases classification precision can be improved using a genetic algorithm. In authorship attribution, in recent years the trend has been towards ever larger word vectors [1,2]. We suggest that this might be a counter-productive step as it can easily lead to inaccuracy caused by overfitting or vector-space sparsity (the curse of dimensionality). In sentiment analysis precision is the main issue as rates of greater than 80–85% are not easy to achieve.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122042429","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: 2
Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces 二进制sdmopso及其在脑机接口信道选择中的应用
2010 UK Workshop on Computational Intelligence (UKCI) Pub Date : 2010-11-09 DOI: 10.1109/UKCI.2010.5625570
N. Al Moubayed, Bashar Awwad Shiekh Hasan, J. Q. Gan, Andrei V. Petrovski, J. Mccall
{"title":"Binary-SDMOPSO and its application in channel selection for Brain-Computer Interfaces","authors":"N. Al Moubayed, Bashar Awwad Shiekh Hasan, J. Q. Gan, Andrei V. Petrovski, J. Mccall","doi":"10.1109/UKCI.2010.5625570","DOIUrl":"https://doi.org/10.1109/UKCI.2010.5625570","url":null,"abstract":"In [1], we introduced Smart Multi-Objective Particle Swarm Optimisation using Decomposition (SDMOPSO). The method uses the decomposition approach proposed in Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D), whereby a multi-objective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. This work customize SDMOSPO to cover binary problems and applies the proposed binary method on the channel selection problem for Brain-Computer Interfaces(BCI).","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115048173","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
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