feature select
18049909
Study on feature select based on coalitional game
Feature selection is an important processing step in machine learning. Most used feature selection methods choose top ranking features without considering the relationships among features. In this paper, the signification of feature selection is introduced, and the goal and evaluation criteria of feature selection are analyzed. The coalitional game theory related to the feature selection is explained. An algorithm of coalitional game based feature selection (CGFS) is presented. This work focus on selecting a sub feature set in which the selected features are coalitional and relevant in order to obtain better classification performance. The experimental results show that CGFS obtains better performance than MI.
2008 International Conference on Neural Networks and Signal Processing
Jihong Liu Soo-Young Lee
125321304
Unsupervised feature selection by self paced learning regularization
Abstract Previous feature selection methods equivalently consider the samples to select important features. However, the samples are often diverse. For example, the outliers should have small or even zero weights while the important samples should have large weights. In this paper, we add a self paced regularization in the sparse feature selection model to reduce the impact of outliers for conducting feature selection. Specifically, the proposed method automatically selects a sample subset which includes the most important samples to build an initial feature selection model, whose generalization ability is then improved by involving other important samples until a robust and generalized feature selection model has been established or all the samples have been used. Experimental results on eight real datasets show that the proposed method outperforms the comparison methods.
Pattern Recognit. Lett.
Wei Zheng Xiaofeng Zhu Guoqiu Wen Yonghua Zhu Hao Yu Jiangzhang Gan
53113264
Binary grasshopper optimisation algorithm approaches for feature selection problems
Abstract Feature Selection (FS) is a challenging machine learning related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V shaped transfer functions, and will be indicated by BGOA S and BGOA V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA M) The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well regarded metaheuristic wrapper based approaches, and six well known filter based (e.g. correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA M methods compared to other similar techniques in the literature.
Expert Syst. Appl.
Majdi M Mafarja Ibrahim Aljarah Hossam Faris Abdelaziz I Hammouri Ala' M Al-Zoubi Seyed Mohammad Mirjalili
53116117
Binary butterfly optimization approaches for feature selection
Abstract In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state of the art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.
Expert Syst. Appl.
Sankalap Arora Priyanka Anand
13756368
Learning a Discriminative Feature Network for Semantic Segmentation
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra class inconsistency and inter class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN) which contains two sub networks: Smooth Network and Border Network. Specifically, to handle the intra class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state of the art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Changqian Yu Jingbo Wang Chao Peng Changxin Gao Gang Yu Nong Sang
59409725
A review of unsupervised feature selection methods
In recent years, unsupervised feature selection methods have raised considerable interest in many research areas; this is mainly due to their ability to identify and select relevant features without needing class label information. In this paper, we provide a comprehensive and structured review of the most relevant and recent unsupervised feature selection methods reported in the literature. We present a taxonomy of these methods and describe the main characteristics and the fundamental ideas they are based on. Additionally, we summarized the advantages and disadvantages of the general lines in which we have categorized the methods analyzed in this review. Moreover, an experimental comparison among the most representative methods of each approach is also presented. Finally, we discuss some important open challenges in this research area.
Artificial Intelligence Review
Saul Solorio-Fernandez Jesus Ariel Carrasco-Ochoa Jose Fco Martinez-Trinidad
13756474
A new feature selection method to improve the document clustering using particle swarm optimization algorithm
Abstract The large amount of text information on the Internet and in modern applications makes dealing with this volume of information complicated. The text clustering technique is an appropriate tool to deal with an enormous amount of text documents by grouping these documents into coherent groups. The document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative features (i.e. noisy, irrelevant, and unnecessary features) which affect the effectiveness of the text clustering technique. The feature selection technique is a primary unsupervised learning method employed to select the informative text features to create a new subset of a document's features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems have been successfully solved using metaheuristic algorithms. This paper proposes a novel feature selection method, namely, feature selection method using the particle swarm optimization (PSO) algorithm (FSPSOTC) to solve the feature selection problem by creating a new subset of informative text features. This new subset of features can improve the performance of the text clustering technique and reduce the computational time. Experiments were conducted using six standard text datasets with several characteristics. These datasets are commonly used in the domain of the text clustering. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with the other well known algorithms i.e. feature selection method using a genetic algorithm to improve the text clustering (FSGATC) and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection.
J. Comput. Sci.
Laith Mohammad Abualigah Ahamad Tajudin Abdul Khader Essam Said Hanandeh
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
125130486
S shaped Binary Whale Optimization Algorithm for Feature Selection
Whale optimization algorithm is one of the recent nature inspired optimization technique based on the behavior of bubble net hunting strategy. In this paper, a novel binary version of whale optimization algorithm (bWOA) is proposed to select the optimal feature subset for dimensionality reduction and classifications problem. The new approach is based on a sigmoid transfer function (S shape) By dealing with the feature selection problem, a free position of the whale must be transformed to their corresponding binary solutions. This transformation is performed by applying an S shaped transfer function in every dimension that defines the probability of transforming the position vectors' elements from 0 to 1 and vice versa and hence force the search agents to move in a binary space. K NN classifier is applied to ensure that the selected features are the relevant ones. A set of criteria are used to evaluate and compare the proposed bWOA S with the native one over eleven different datasets. The results proved that the new algorithm has a significant performance in finding the optimal feature.
Abdelazim G Hussien Aboul Ella Hassanien Essam H Houssein Siddhartha Bhattacharyya Mohamed Amin
52915238
Hierarchical Feature Selection for Random Projection
Random projection is a popular machine learning algorithm, which can be implemented by neural networks and trained in a very efficient manner. However, the number of features should be large enough when applied to a rather large scale data set, which results in slow speed in testing procedure and more storage space under some circumstances. Furthermore, some of the features are redundant and even noisy since they are randomly generated, so the performance may be affected by these features. To remedy these problems, an effective feature selection method is introduced to select useful features hierarchically. Specifically, a novel criterion is proposed to select useful neurons for neural networks, which establishes a new way for network architecture design. The testing time and accuracy of the proposed method are improved compared with traditional methods and some variations on both classification and regression tasks. Extensive experiments confirm the effectiveness of the proposed method.
IEEE Transactions on Neural Networks and Learning Systems
Qi Wang Jia Wan Feiping Nie Bo Liu Chenggang Clarence Yan Xuelong Li