survey feature selection iclr
133392392
Feature selection with multi view data: A survey
Abstract This survey aims at providing a state of the art overview of feature selection and fusion strategies, which select and combine multi view features effectively to accomplish associated tasks. The existing literatures on feature selection approaches are classified into three categories including filter method, wrapper method, and embedded method. Based on the feature selection methods mentioned above, feature level fusion or known as low level fusion methodology is further investigated from the perspective of the basic concept, procedure, and applications in analysis tasks as presented in the literatures. Moreover, several distinctive issues that influence the information fusion process such as the use of correlation, confidence level, synchronization, and the optimal features are also emphasized. Finally, we present the adaptive multi view issues for further research in the area of feature selection and fusion by learning view specific weights to each view data automatically.
Inf. Fusion
Rui Zhang Feiping Nie Xuelong Li Xian Wei
195740144
A Survey on Hybrid Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification
The emergence of DNA Microarray technology has enabled researchers to analyze the expression level of thousands of genes simultaneously. The Microarray data analysis is the process of finding the most informative genes as well as remove redundant and irrelevant genes. One of the most important applications of the Microarray data analysis is cancer classification. However, the curse of dimensionality and the curse of sparsity make classifying gene expression profiles a challenging task. One of the most effective methods to overcome these challenges is feature (gene) selection. In this paper, we aim to review and compare the most recent hybrid approaches that employ bio inspired evolutionary methods as the wrapper method.
IEEE Access
Nada Almugren Hala M Alshamlan
212555045
Survey: Feature Selection Technique Impact for Internet Traffic Classification Using Naive Bayesian
Internet traffic defines as the density of data or information presented on the Internet or in another language we can say it's a flow of data on the internet. Internet traffic classification has power to solve many network difficulties and manage different type of network problems. There are some basic functions provided to government, Internet service providers (ISPs) and network administrator through Internet traffic classification. Machine learning approaches overcome many problems of traditional approaches of internet traffic classification. In supervised approaches, we discuss five well known supervised machine learning approaches these are Naive Bayes, Feed Forward Neural Network, Bayes Net, RBF and C4.5 decision tree approach. Keywords ISP, ML, DAG, RBF, FCBF, BOF|
Satish Chadokar Ashish Kumbhare
195697776
A Brief Survey on Nature Inspired Metaheuristics for Feature Selection in Classification in this Decade
Feature selection (FS) has been a hot topic in the past few decades. More and more researches spring up due to the fast development of data and computer science. Feature selection as a preprocessing technique plays an important role in improving the efficiency of data mining and analysis over substantial real world applications. This paper provides a brief survey on feature selection based on nature inspired metaheuristics for classification in this decade to detail the newest researches. The main challenges facing researchers are concluded as accuracy, stability, scalability and computational cost. Related work trying to solve these challenges is discussed.
2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC)
Wei Liu Jianyu Wang
199511374
Feature selection in image analysis: a survey
Image analysis is a prolific field of research which has been broadly studied in the last decades, successfully applied to a great number of disciplines. Since the apparition of Big Data, the number of digital images is explosively growing, and a large amount of multimedia data is publicly available. Not only is it necessary to deal with this increasing number of images, but also to know which features extract from them, and feature selection can help in this scenario. The goal of this paper is to survey the most recent feature selection methods developed and/or applied to image analysis, covering the most popular fields such as image classification, image segmentation, etc. Finally, an experimental evaluation on several popular datasets using well known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate comparative studies for the research community.
Artificial Intelligence Review
Veronica Bolon-Canedo Beatriz Remeseiro
85447653
Performance comparison of linear and non linear feature selection methods for the analysis of large survey datasets
Large survey databases for aging related analysis are often examined to discover key factors that affect a dependent variable of interest. Typically, this analysis is performed with methods assuming linear dependencies between variables. Such assumptions however do not hold in many cases, wherein data are linked by way of non linear dependencies. This in turn requires applications of analytic methods, which are more accurate in identifying potentially non linear dependencies. Here, we objectively compared the feature selection performance of several frequently used linear selection methods and three non linear selection methods in the context of large survey data. These methods were assessed using both synthetic and real world datasets, wherein relationships between the features and dependent variables were known in advance. In contrast to linear methods, we found that the non linear methods offered better overall feature selection performance than linear methods in all usage conditions. Moreover, the performance of the non linear methods was more stable, being unaffected by the inclusion or exclusion of variables from the datasets. These properties make non linear feature selection methods a potentially preferable tool for both hypothesis driven and exploratory analyses for aging related datasets.
PloS one
Olga Krakovska Gregory J Christie Andrew Sixsmith Martin Ester Sylvain Moreno
86622307
Feature Selection Algorithms for Data Mining Classification: A Survey
Objectives: This study summarizes the feature selection process, its importance, different types of feature selection algorithms such as Filter, Wrapper and Hybrid. Moreover, it analyses some of the existing popular feature selection algorithms through a literature survey and also addresses the strengths and challenges of those algorithms. Methods/ Statistical Analysis: When there are many methods are in hand to be obtained, then Review of Literature is the best approach to learn about existing methods before going for a new model. Findings: Feature selection is a predominant preprocessing strategy in Data Mining, which helps in advancing the performance of mining, by selecting only the relevant features and avoiding the redundant features. There are plenty Feature Selection algorithms developed and used by most researchers. But still it is an emerging area in machine learning to be focused for data mining and analysis process for pattern recognition. Many feature selection algorithms confront severe challenges in terms of effectiveness and efficiency, because of recent increase in data variety and velocity. Different types of feature selection algorithms are available in literature such as Filter based, Wrapper based and Hybrid algorithms. Moreover, analyses some of the existing popular feature selection algorithms through a literature survey, also addresses the strengths and challenges of those algorithms. Application/Improvements: There is a need for an effective unified framework, which should provide feature selection for any size of dataset without noisy data, low computational complexity and highest accuracy. *Author for correspondence
Indian Journal of Science and Technology
Nagappan Krishnaveni V Radha
192675994
Swarm Intelligence Based Feature Selection for High Dimensional Classification: A Literature Survey
Feature selection is an important and challenging task in machine learning and data mining techniques to avoid the curse of dimensionality and maximize the classification accuracy. Moreover, feature selection helps to reduce computational complexity of learning algorithm, improve prediction performance, better data understanding and reduce data storage space. Swarm intelligence based feature selection approach enables to find an optimal feature subset from an extremely large dimensionality of features for building the most accurate classifier model. There is still a type of researches that is not done yet in data mining. In this paper, the utilization of swarm intelligence algorithms for feature selection process in high dimensional data focusing on medical data classification is form the subject matter. The results shows that swarm intelligence algorithms reviewed based on state of the art literature have a promising capability that can be applied in feature selections techniques. The significance of this work is to present the comparison and various alternatives of swarm algorithms to be applied in feature selections for high dimensional classification.
Thinzar Saw Phyu Hninn Myint
215816658
Feature Selection Techniques for Cancer Classification applied to Microarray Data: A survey
In multidimensional microarrays, data that collect gene expression profiles that fulfill the state of the cell at the molecular level. Feature selection and extraction have become an obvious need for the analysis of this microarray. There are many different methods for selecting and extracting attributes, and they are widely used. One of the serious tasks is to learn how to extract useful information from huge microarrays datasets with complex relationships between different genes. These methods are aimed at removing excess and irrelevant traits and extruding marker genes that effectively maintain classification accuracy. This report gives an overview of the various ways of performing dimensional reduction methods that were used in these microarrays to select important features and presents a comparison between them. The advantages and disadvantages of several methods are described in order to show an obvious idea of when to use each of them to save computational time and resources.
2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS)
Mohammed Qaraad Souad Amjad Hanaa Fathi Ibrahim I M Manhrawy
215875113
Why Feature Selection in Data Mining Is Prominent? A Survey
Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today's world situations. Current feature selection techniques primarily concentrate on obtaining relevant attributes. This paper presents the notions of feature relevance, redundancy, evaluation criteria, and literature survey on the feature selection approaches in the different areas by many researchers. This paper supports to choose feature selection techniques without identifying the knowledge of every algorithm.
AISGSC 2019
M Durairaj T S Poornappriya