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Performing Feature Selection with ACO [chapter]

Richard Jensen
2006 Studies in Computational Intelligence  
This chapter presents a feature selection mechanism based on Ant Colony Optimization in an attempt to combat this.  ...  The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process.  ...  The author would like to thank Qiang Shen for his support during the development of the ideas presented in this chapter.  ... 
doi:10.1007/978-3-540-34956-3_3 fatcat:zu2xz7hying5ncthlfg2o45gbi

Rough set based feature selection: A Review

Javad Rahimipour Anaraki, Mahdi Eftekhari
2013 The 5th Conference on Information and Knowledge Technology  
Alternative search mechanisms are also highly important in rough set feature selection.  ...  Extensions to the traditional rough set approach are discussed, including recent selection methods based on tolerance rough sets, variable precision rough sets and fuzzy-rough sets.  ...  Experimentation reported in (Wang et al., 2006) demonstrates the utility of PSObased rough set feature selection.  ... 
doi:10.1109/ikt.2013.6620083 fatcat:pfkzxaaymjemroxv7hdkmjti5e

An Experimental Investigation of Micro- and Macrocracking Mechanisms in Rocks by Freeze‒Thaw Cycling

Vikram Maji
2020 Figshare  
The fracture of rock during freezing and thawing poses a serious threat to rock slope stability and represents an important geohazard in cold regions.  ...  To investigate the mechanisms of cracking, two physical modelling experiments supplemented by compressive tests were performed on specimens of chalk and sandstone, monitoring and imaging micro- and macroscale  ...  Yoon et al. (2000) conducted an experimental investigation on corroded reinforced concrete (RC) beams and concluded that the frequency of AE events and rate of AE generation depend on the degree of corrosion  ... 
doi:10.6084/m9.figshare.12643244.v1 fatcat:ssouwlkaqjazfllvdqq2hzh6wq

Fuzzy-Rough Sets Assisted Attribute Selection

Richard Jensen, Qiang Shen
2007 IEEE transactions on fuzzy systems  
This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics.  ...  However, the main limitation of rough set-based attribute selection in the literature is the restrictive requirement that all data is discrete.  ...  This paper presents a method, fuzzy-rough feature selection (FRFS), that employs fuzzy-rough sets to provide a means by which discrete or real-valued noisy data (or a mixture of both) can be effectively  ... 
doi:10.1109/tfuzz.2006.889761 fatcat:bvkjsickofe4hopiq3ti5d2uly

A Novel Approach of Rough Conditional Entropy-Based Attribute Selection for Incomplete Decision System

Tao Yan, Chongzhao Han
2014 Mathematical Problems in Engineering  
Furthermore, some important properties of rough conditional entropy are derived and three attribute selection approaches are constructed, including an exhaustive search strategy approach, a heuristic search  ...  Pawlak's classical rough set theory has been applied in analyzing ordinary information systems and decision systems.  ...  Moreover, the authors thank the UCI Repository of Machine Learning Database at the University of California for providing the experiment data sets.  ... 
doi:10.1155/2014/728923 fatcat:7fxsc4ludfemnkoonb7thwg3cy

Rough ACO: A Hybridized Model for Feature Selection in Gene Expression Data

Debahuti Mishra, Dr. Amiya Kumar Rath, Dr. Milu Acharya, Tanushree Jena
2010 International journal of computer and communication technology  
The proposed method is successfully applied for choosing the best feature combinations and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data  ...  the ACO hybridized with Rough Set Theory.  ...  The general information of selected data set is shown in table 1.The performance of feature selection of Rough Set based algorithms is showed on the Table2.The experimental result shows that feature selection  ... 
doi:10.47893/ijcct.2010.1009 fatcat:23exbcen2rdwdhlpgehhhqocdm

Fuzzy decision tree using soft discretization and a genetic algorithm based feature selection method

Min Chen, Simone A. Ludwig
2013 2013 World Congress on Nature and Biologically Inspired Computing  
The selection of important features of a data set is a very important preprocessing task in order to obtain higher accuracy of the classifier as well as to speed up the learning task.  ...  Therefore, we are applying a feature selection method that is based on the ideas of mutual information and genetic algorithms.  ...  In general, feature selection can improve the scalability, efficiency and accuracy of classifiers. Therefore, the proposed FDT with GA feature selection is investigated.  ... 
doi:10.1109/nabic.2013.6617869 dblp:conf/nabic/ChenL13 fatcat:zrdxshyum5dzdmjitrnzfjje6y

Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection

Ruonan Zhao, Lize Gu, Xiaoning Zhu
2019 Applied Sciences  
The feature selection based on fuzzy rough sets can process a large number of continuous and discrete data to reduce the data dimension, making the selected feature subset highly correlated with the classification  ...  In this paper, a new method of fuzzy rough feature selection is proposed which combines the membership function determination method of fuzzy c-means clustering and fuzzy equivalence to the original selection  ...  Acknowledgments: The authors would like to thank the reviewers for their helpful advice. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9040679 fatcat:3772g4ystna7pmsfjffnnczisy

Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring

Q SHEN
2004 Pattern Recognition  
This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss.  ...  Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-, PCA-and random-based methods.  ...  The authors are very grateful to Alexios Chouchoulas and David Robertson for their support.  ... 
doi:10.1016/s0031-3203(03)00424-2 fatcat:m6ll4nccebfrxap7jxu5znh6hq

Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring

Qiang Shen, Richard Jensen
2004 Pattern Recognition  
This paper proposes a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, to avoid this information loss.  ...  Experimental results, of applying the present work to complex systems monitoring, show that fuzzy-rough selection is more powerful than conventional entropy-, PCA-and random-based methods.  ...  The authors are very grateful to Alexios Chouchoulas and David Robertson for their support.  ... 
doi:10.1016/j.patcog.2003.10.016 fatcat:j3dmvpyaxrditpdhdhqj77g5iu

Sensor Selection for IT Infrastructure Monitoring [chapter]

Gergely János Paljak, Imre Kocsis, Zoltán Égel, Dániel Tóth, András Pataricza
2010 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
The selection of a compact, but sufficiently characteristic set of control variables is one of the core problems both for design and run-time complexity.  ...  Most results in the literature are based on a single algorithm for variable selection, but our measurements indicate that no single algorithm can generate faithful estimates for all the different operational  ...  This paper proposes to combine linear estimators with the powerful minimum-Redundancy-Maximum-Relevance (mRMR) nonlinear feature selection scheme for the selection of such a small set of metrics that still  ... 
doi:10.1007/978-3-642-11482-3_9 fatcat:kiwsyjgukfhltjsthd4qjf4ksu

An Experimental Investigation of the Wall Deposition of Milk Powder in a Pilot-Scale Spray Dryer

L. Ozmen, T. A. G. Langrish
2003 Drying Technology  
Examiner 2 Comment 2: It is noted that the comparison between glass transition point and sticky point has been made for only one material.  ...  It is clear that the drying performance is limited by the equilibrium between gas and solids, so that 0 o angle results in the least wall deposition with an insignificant penalty regarding the drying effectiveness  ...  These values provide a discrete approximation to the flowfield. These results can then be visualised.  ... 
doi:10.1081/drt-120023179 fatcat:qc5nvh2lqrfefb5edbe6luronu

Training Data Selection for Accuracy and Transferability of Interatomic Potentials [article]

David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers, Carlos Z. Pereyra, Aidan P. Thompson, Danny Perez
2022 arXiv   pre-print
set for tungsten in an automated manner, i.e., without any human intervention.  ...  For comparison, a corresponding family of potentials were also trained on an expert-curated dataset for tungsten.  ...  Acknowledgements The development of the entropy maximization method and the generation of the training data was supported by the Exascale Computing Project (  ... 
arXiv:2201.09829v2 fatcat:qlvtlwet3fd5dln7kteowycbha

Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding

Liqiang He, Shuhua Xiong, Ruolan Yang, Xiaohai He, Honggang Chen
2022 Sensors  
The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).  ...  the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units' (sub-CUs') information entropy  ...  The experiments were performed on an Intel core i5-3470 CPU.  ... 
doi:10.3390/s22155523 pmid:35898027 pmcid:PMC9331267 fatcat:3wym4kqpcjamtaxetfe2rf3j2m

Finding fuzzy-rough reducts with fuzzy entropy

Neil Mac Parthalain, Richard Jensen, Qiang Shen
2008 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)  
This paper presents three novel feature selection techniques employing fuzzy entropy to locate fuzzy-rough reducts.  ...  This approach is compared with two other fuzzy-rough feature selection approaches which utilise other measures for the selection of subsets.  ...  CONCLUSIONS This paper has presented three new techniques for fuzzyrough feature selection based on the use of fuzzy entropy as an evaluation metric for the fuzzy-rough lower approximations.  ... 
doi:10.1109/fuzzy.2008.4630537 dblp:conf/fuzzIEEE/MacParthalainJS08 fatcat:hvh4f6jsh5eo5kba7ozhrame6a
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