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A New Hybrid Feature Selection Method Using T-test and Fitness Function

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Computers, Materials & Continua 2021年第9期68卷 3997-4016页

作者： Husam Ali Abdulmohsin Hala Bahjat Abdul Wahab Abdul Mohssen Jaber Abdul Hossen Department of Computer Science Faculty of ScienceUniversity of BaghdadBaghdadIraq Faculty of Computer Science Technology UniversityBaghdadIraq Department of Computer Science Al-Turath University CollegeBaghdadIraq

Feature selection(FS)(or feature dimensional reduction,or feature optimization)is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system complexity.FS reduces the number of features extracted in the feature extraction phase by reducing highly correlated features,retaining features with high information gain,and removing features with no weights in classification.In this work,an FS filter-type statistical method is designed and implemented,utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value(QoPV).The approach utilizes the well-designed fitness function to calculate the strength of recognition value(SoRV).The two values are used to rank all features according to the final weight(FW)calculated for each feature subset using a function that prioritizes feature subsets with high SoRV values.An FW is assigned to each feature subset,and those with FWs less than a predefined threshold are removed from the feature subset domain.Experiments are implemented on three datasets:Ryerson Audio-Visual Database of Emotional Speech and Song,Berlin,and Surrey Audio-Visual Expressed Emotion.The performance of the F-test and F-score FS methods are compared to those of the proposed method.Tests are also conducted on a system before and after deploying the FS methods.Results demonstrate the comparative efficiency of the proposed method.The complexity of the system is calculated based on the time overhead required before and after FS.Results show that the proposed method can reduce system complexity.

关键词： Feature selection dimensional reduction feature optimization pattern recognition classification t-test

Inhomogeneous Incompressible Navier-Stokes Equations on Thin Domains

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Communications in Mathematics and Statistics 2020年第2期8卷 239-253页

作者： Yongzhong Sun Shifang Wang Department of Mathematics Nanjing UniversityNanjing 210093People’s Republic of China

We consider the inhomogeneous incompressible Navier-Stokes equation on thin domains T^(2)×∈T,∈→0.It is shown that the weak solutions on T^(2)×∈T converge to the strong/weak solutions of the 2D inhomogeneous inco... 详细信息

关键词： Inhomogeneous incompressible Navier-Stokes equation Thin domain limit dimensional reduction Relative energy

Outlier Detection Algorithm Based on Iterative Clustering

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Journal of Donghua University(English Edition) 2015年第4期32卷 554-558页

作者：古平罗辛杨瑞龙张程 Department of Computer Science and Technology Chongqing University

A novel approach for outlier detection with iterative clustering( ICOD) in diverse subspaces is proposed. The proposed methodology comprises two phases,iterative clustering and outlier factor computation. During the clustering phase, multiple clusterings are detected alternatively based on an optimization procedure that incorporates terms for cluster quality and novelty relative to existing solution. Once new clusters are detected,outlier factors can be estimated from a new definition for outliers( cluster based outlier), which provides importance to the local data behavior. Experiment shows that the proposed algorithm can detect outliers which exist in different clusterings effectively even in high dimensional data sets.

关键词： clustering outlier detection dimensional reduction

Dimension reduction Based on Sampling

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国际计算机前沿大会会议论文集 2023年第1期 207-220页

作者： Zhuping Li Donghua Yang Mengmeng Li Haifeng Guo Tiansheng Ye Hongzhi Wang Harbin Institute of Technology HarbinChina ConDB BeijingChina

Dimension reduction provides a powerful means of reducing the number of random variables under consideration.However,there were many similar tuples in large datasets,and before reducing the dimension of the dataset,we removed some similar tuples to retain the main information of the dataset while accelerating the dimension reduc-tion.Accordingly,we propose a dimension reduction technique based on biased sampling,a new procedure that incorporates features of both dimensional reduction and biased sampling to obtain a computationally efficient means of reducing the number of random variables under consid-eration.In this paper,we choose Principal Components Analysis(PCA)as the main dimensional reduction algorithm to study,and we show how this approach works.

关键词： PCA dimensional reduction biased sampling

LLE Based Pivot Selection for Similarity Search of Biological Data

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LLE Based Pivot Selection for Similarity Search of Biologica...

A Novel Subspace Outlier Detection Approach in High dimensional Data Sets

2012 National Conference on Information Technology and Computer Science

作者： Kewei Ma,Honglong Xu,Pang Yue,Fuli Lei,Sheng Liu,Rui Mao Guangdong Key Laboratory of Popular High Performance Computers Shenzhen Key Laboratory of Service Computing and Applications.Shenzhen University Shenzhen,China Jiaxin Han Oracle Corporation Redwood City,CA,USA

Distance-based indexing is a widely used technique for general purpose search.Pivot selection is the most crucial step of bulkloading a metric-space indexing tree.Current pivot selection methods are mainly based on linear methods.A non-linear method based on Locally Linear Embedding is proposed.Empirical results demonstrate that the performance of new method is superior to existing methods.

关键词： similarity search metric-space indexing pivot selection locally linear embedding dimensional reduction

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A Novel Subspace Outlier Detection Approach in High Dimensio...

The relationship between four—dimensional θ=π Yang—Mills theory and the two—dimensional Wess —Zumino—Novikov—Witten model

2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Jinsong Leng School of Computer and Security Edith Cowan University

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspace-based outlier detection methods identify outliers by searching for abnormal sparse density units in subspaces. In this paper, we present a novel approach for finding outliers in the ’interesting’ subspaces. The interesting subspaces are strongly correlated with `good’ clusters. This approach aims to group the meaningful subspaces and then identify outliers in the projected subspaces. In doing so, an extension to the subspace-based clustering algorithm is proposed so as to find the ’good’ subspaces, and then outliers are identified in the projected subspaces using some classical outlier detection techniques such as distance-based and density-based algorithms. Comprehensive case studies are conducted using various types of subspace clustering and outlier detection algorithms. The experimental results demonstrate that the proposed method can detect outliers effectively and efficiently in high dimensional data sets.

关键词： Data Mining Subspace Clustering Outlier Detection dimensional reduction

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Chinese Physics B 2002年第8期11卷 785-789页

作者：寇谡鹏 DepartmentofPhysics BeijingNormalUniversityBeijing100875China

Used the dimensional reduction in the sense of Parisi and Sourlas, the gauge fixing term of the four-dimensionalYang-Mills field without the theta term is reduced to a two-dimensional principal chiral model. By adding the θ term(θ = π), the two-dimensional principal chiral model changes into the two-dimensional level 1 Wess-Zumino-Novikov-Witten model. The non-trivial fixed point indicates that Yang-Mills theory at θ = π is a critical theory without massgap and confinement.

关键词： Yang-Mills field dimensional reduction Wess-Zumino-Novikov-Witten model