scholarly journals Clustering Based Classification and Analysis of Data

2014 ◽  
pp. 280-283
Author(s):  
NEERAJ SAHU ◽  
D. S. RAJPUT ◽  
R. S. THAKUR ◽  
G. S. THAKUR
Keyword(s):  
Feature Selection ◽  
Document Classification ◽  
Experimental Results ◽  
Data Sets ◽  
Group Data ◽  
Document Collection ◽  
Text Preprocessing

This paper presents Clustering Based Document classification and analysis of data. The proposed Clustering Based classification and analysis of data approach is based on Unsupervised and Supervised Document Classification. In this paper Unsupervised Document and Supervised Document Classification are used. In this approach Document collection, Text Preprocessing, Feature Selection, Indexing, Clustering Process and Results Analysis steps are used. Twenty News group data sets [20] are used in the Experiments. For experimental results analysis evaluated using the Analytical SAS 9.0 Software is used. The Experimental Results show the proposed approach out performs.

A Novel Biased Diversity Ranking Model for Query-Oriented Multi-Document Summarization

2013 ◽  
Vol 380-384 ◽  
pp. 2811-2816
Author(s):  
Kai Lei ◽  
Yi Fan Zeng
Keyword(s):  
Information Needs ◽  
Experimental Results ◽  
Data Sets ◽  
Manifold Ranking ◽  
Document Summarization ◽  
Benchmark Data ◽  
Ranking Model ◽  
Document Collection ◽  
High Prestige

Query-oriented multi-document summarization (QMDS) attempts to generate a concise piece of text byextracting sentences from a target document collection, with the aim of not only conveying the key content of that corpus, also, satisfying the information needs expressed by that query. Due to its great applicable value, QMDS has been intensively studied in recent decades. Three properties are supposed crucial for a good summary, i.e., relevance, prestige and low redundancy (orso-called diversity). Unfortunately, most existing work either disregarded the concern of diversity, or handled it with non-optimized heuristics, usually based on greedy sentences election. Inspired by the manifold-ranking process, which deals with query-biased prestige, and DivRank algorithm which captures query-independent diversity ranking, in this paper, we propose a novel biased diversity ranking model, named ManifoldDivRank, for query-sensitive summarization tasks. The top-ranked sentences discovered by our algorithm not only enjoy query-oriented high prestige, more importantly, they are dissimilar with each other. Experimental results on DUC2005and DUC2006 benchmark data sets demonstrate the effectiveness of our proposal.

scholarly journals Semi-Supervised Feature Selection with Adaptive Discriminant Analysis

2019 ◽  
Vol 33 ◽  
pp. 10083-10084
Author(s):  
Weichan Zhong ◽  
Xiaojun Chen ◽  
Guowen Yuan ◽  
Yiqin Li ◽  
Feiping Nie
Keyword(s):  
Feature Selection ◽  
Discriminant Analysis ◽  
Iterative Method ◽  
Projection Matrix ◽  
Experimental Results ◽  
Data Sets ◽  
Similarity Matrix ◽  
Selection Methods ◽  
Projected Distance

In this paper, we propose a novel Adaptive Discriminant Analysis for semi-supervised feature selection, namely SADA. Instead of computing fixed similarities before performing feature selection, SADA simultaneously learns an adaptive similarity matrix S and a projection matrix W with an iterative method. In each iteration, S is computed from the projected distance with the learned W and W is computed with the learned S. Therefore, SADA can learn better projection matrix W by weakening the effect of noise features with the adaptive similarity matrix. Experimental results on 4 data sets show the superiority of SADA compared to 5 semisupervised feature selection methods.

Multi-view feature selection via sparse tensor regression

2021 ◽  
pp. 2150020
Author(s):  
Haoliang Yuan ◽  
Sio-Long Lo ◽  
Ming Yin ◽  
Yong Liang
Keyword(s):  
Feature Selection ◽  
Computational Complexity ◽  
Regression Model ◽  
Optimization Algorithm ◽  
High Order ◽  
Experimental Results ◽  
Data Sets ◽  
Tensor Structure ◽  
Tensor Regression ◽  
Iterative Optimization Algorithm

In this paper, we propose a sparse tensor regression model for multi-view feature selection. Apart from the most of existing methods, our model adopts a tensor structure to represent multi-view data, which aims to explore their underlying high-order correlations. Based on this tensor structure, our model can effectively select the meaningful feature set for each view. We also develop an iterative optimization algorithm to solve our model, together with analysis about the convergence and computational complexity. Experimental results on several popular multi-view data sets confirm the effectiveness of our model.

scholarly journals Worst-Case Discriminative Feature Selection

2019 ◽  
Author(s):  
Shuangli Liao ◽  
Quanxue Gao ◽  
Feiping Nie ◽  
Yang Liu ◽  
Xiangdong Zhang
Keyword(s):  
Feature Selection ◽  
Critical Role ◽  
Correlation Coefficients ◽  
Real Data ◽  
Experimental Results ◽  
Data Sets ◽  
Fisher Score ◽  
Worst Case ◽  
Discriminative Feature ◽  
New Perspective

Feature selection plays a critical role in data mining, driven by increasing feature dimensionality in target problems. In this paper, we propose a new criterion for discriminative feature selection, worst-case discriminative feature selection (WDFS). Unlike Fisher Score and other methods based on the discriminative criteria considering the overall (or average) separation of data, WDFS adopts a new perspective called worst-case view which arguably is more suitable for classification applications. Specifically, WDFS directly maximizes the ratio of the minimum of between-class variance of all class pairs over the maximum of within-class variance, and thus it duly considers the separation of all classes. Otherwise, we take a greedy strategy by finding one feature at a time, but it is very easy to implement. Moreover, we also utilize the correlation between features to help reduce the redundancy and extend WDFS to uncorrelated WDFS (UWDFS). To evaluate the effectiveness of the proposed algorithm, we conduct classification experiments on many real data sets. In the experiment, we respectively use the original features and the score vectors of features over all class pairs to calculate the correlation coefficients, and analyze the experimental results in these two ways. Experimental results demonstrate the effectiveness of WDFS and UWDFS.

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

scholarly journals DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

AbstractData variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

scholarly journals Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1816
Author(s):  
Hailun Xie ◽  
Li Zhang ◽  
Chee Peng Lim ◽  
Yonghong Yu ◽  
Han Liu
Keyword(s):  
Feature Selection ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Particle Swarm Optimisation ◽  
Data Sets ◽  
Discriminative Feature ◽  
Advanced Search ◽  
Selection For ◽  
Selection Tasks ◽  
New Strategies

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

Bayesian Classifier for Sparsity-Promoting Feature Selection

2015 ◽  
Vol 29 (06) ◽  
pp. 1550022 ◽  
Author(s):  
Danlei Xu ◽  
Lan Du ◽  
Hongwei Liu ◽  
Penghui Wang
Keyword(s):  
Feature Selection ◽  
Synthetic Data ◽  
Original Data ◽  
Radar Data ◽  
Bayesian Classifier ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Classification Boundary ◽  
Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

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