scholarly journals A Unified View of Causal and Non-causal Feature Selection

2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li
Keyword(s):  
Feature Selection ◽  
Bayesian Network ◽  
Synthetic Data ◽  
Selection Methods ◽  
Bayesian Network Model ◽  
Real World Data ◽  
Feature Sets ◽  
Unified View ◽  
Optimal Feature ◽  
Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

Feature Selection Using Random Forest Algorithm to Diagnose Tuberculosis From Lung CT Images

2021 ◽  
pp. 92-100
Author(s):  
Beaulah Jeyavathana Rajendran ◽  
Kanimozhi K. V.
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
The Body ◽  
Support Vector ◽  
Feature Descriptor ◽  
Feature Sets ◽  
Modified Particle Swarm Optimization ◽  
Tuberculosis Disease ◽  
Optimal Feature ◽  
Lung Ct

Tuberculosis is one of the hazardous infectious diseases that can be categorized by the evolution of tubercles in the tissues. This disease mainly affects the lungs and also the other parts of the body. The disease can be easily diagnosed by the radiologists. The main objective of this chapter is to get best solution selected by means of modified particle swarm optimization is regarded as optimal feature descriptor. Five stages are being used to detect tuberculosis disease. They are pre-processing an image, segmenting the lungs and extracting the feature, feature selection and classification. These stages that are used in medical image processing to identify the tuberculosis. In the feature extraction, the GLCM approach is used to extract the features and from the extracted feature sets the optimal features are selected by random forest. Finally, support vector machine classifier method is used for image classification. The experimentation is done, and intermediate results are obtained. The proposed system accuracy results are better than the existing method in classification.

scholarly journals CUR: Group Profiling with Community-based Users’ Representation

2018 ◽  
Author(s):  
João Emanoel Ambrósio Gomes ◽  
Ricardo B. C. Prudêncio ◽  
André C. A. Nascimento
Keyword(s):  
Feature Selection ◽  
Relevant Feature ◽  
Selection Methods ◽  
Community Based ◽  
Feature Sets ◽  
Network Community ◽  
Representation Method ◽  
Selection Algorithms ◽  
Content Information ◽  
Better Than

Group profiling methods aim to construct a descriptive profile for communities in social networks. Before the application of a profiling algorithm, it is necessary to collect and preprocess the users’ content information, i.e., to build a representation of each user in the network. Usually, existing group profiling strategies define the users’ representation by uniformly processing the entire content information in the network, and then, apply traditional feature selection methods over the user features in a group. However, such strategy may ignore specific characteristics of each group. This fact can lead to a limited representation for some communities, disregarding attributes which are relevant to the network perspective and describing more clearly a particular community despite the others. In this context, we propose the community-based user’s representation method (CUR). In this proposal, feature selection algorithms are applied over user features for each network community individually, aiming to assign relevant feature sets for each particular community. Such strategy will avoid the bias caused by larger communities on the overall user representation. Experiments were conducted in a co-authorship network to evaluate the CUR representation on different group profiling strategies and were assessed by hu- man evaluators. The results showed that profiles obtained after the application of the CUR module were better than the ones obtained by conventional users’ representation on an average of 76.54% of the evaluations.

scholarly journals Multi-Label Feature Selection Method Based on Dynamic Weight

2021 ◽  
Author(s):  
Ping Zhang ◽  
Jiyao Sheng ◽  
Wanfu Gao ◽  
Juncheng Hu ◽  
Yonghao Li
Keyword(s):  
Feature Selection ◽  
Dynamic Change ◽  
Feature Selection Method ◽  
Selection Method ◽  
Data Sets ◽  
Selection Methods ◽  
Real World Data ◽  
Amount Of Information ◽  
The Difference ◽  
Classification Information

Abstract Multi-label feature selection attracts considerable attention from multi-label learning. Information-theory based multi-label feature selection methods intend to select the most informative features and reduce the uncertain amount of information of labels. Previous methods regard the uncertain amount of information of labels as constant. In fact, as the classification information of the label set is captured by features, the remaining uncertainty of each label is changing dynamically. In this paper, we categorize labels into two groups: one contains the labels with few remaining uncertainty, which means that most of classification information with respect to the labels has been obtained by the already-selected features; another group contains the labels with extensive remaining uncertainty, which means that the classification information of these labels is neglected by already-selected features. Feature selection aims to select the new features with highly relevant to the labels in the second group. Existing methods do not distinguish the difference between two label groups and ignore the dynamic change amount of information of labels. To this end, a Relevancy Ratio is designed to clarify the dynamic change amount of information of each label under the condition of the already-selected features. Afterwards, a Weighted Feature Relevancy is defined to evaluate the candidate features. Finally, a new multi-label Feature Selection method based on Weighted Feature Relevancy (WFRFS) is proposed. The experiments obtain encouraging results of WFRFS in comparison to six multi-label feature selection methods on thirteen real-world data sets.

scholarly journals Multi-label Feature Selection via Global Relevance and Redundancy Optimization

2020 ◽  
Author(s):  
Jia Zhang ◽  
Yidong Lin ◽  
Min Jiang ◽  
Shaozi Li ◽  
Yong Tang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Empirical Studies ◽  
Data Sets ◽  
Selection Methods ◽  
Label Data ◽  
Optimization Framework ◽  
Feature Relevance ◽  
Optimal Feature Selection ◽  
Effectiveness And Efficiency ◽  
Optimal Feature

Information theoretical based methods have attracted a great attention in recent years, and gained promising results to deal with multi-label data with high dimensionality. However, most of the existing methods are either directly transformed from heuristic single-label feature selection methods or inefficient in exploiting labeling information. Thus, they may not be able to get an optimal feature selection result shared by multiple labels. In this paper, we propose a general global optimization framework, in which feature relevance, label relevance (i.e., label correlation), and feature redundancy are taken into account, thus facilitating multi-label feature selection. Moreover, the proposed method has an excellent mechanism for utilizing inherent properties of multi-label learning. Specially, we provide a formulation to extend the proposed method with label-specific features. Empirical studies on twenty multi-label data sets reveal the effectiveness and efficiency of the proposed method. Our implementation of the proposed method is available online at: https://jiazhang-ml.pub/GRRO-master.zip.

scholarly journals Feature Selection for Interpatient Supervised Heart Beat Classification

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
G. Doquire ◽  
G. de Lannoy ◽  
D. François ◽  
M. Verleysen
Keyword(s):  
Feature Selection ◽  
Domain Knowledge ◽  
Heart Beat ◽  
Classification Models ◽  
Feature Sets ◽  
Beat Classification ◽  
Feature Selection Techniques ◽  
Optimal Feature

Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier. As a consequence, the results obtained by these models can be suboptimal and difficult to interpret. In this work, feature selection techniques are considered to extract optimal feature subsets for state-of-the-art ECG classification models. The performances are evaluated on real ambulatory recordings and compared to previously reported feature choices using the same models. Results indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features.

scholarly journals Ensemble Classification Based on Feature Selection for Environmental Sound Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Shuai Zhao ◽  
Yan Zhang ◽  
Haifeng Xu ◽  
Te Han
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Ensemble Classification ◽  
Sound Recognition ◽  
Selection Methods ◽  
Improved Method ◽  
Environmental Sound ◽  
Selection For ◽  
Optimal Feature ◽  
Environmental Sound Recognition

Environmental sound recognition has been a hot topic in the domain of audio recognition. How to select the optimal feature subsets and enhance the performance of classification precisely is an urgent problem to be solved. Ensemble learning, a new kind of method presented recently, has been an effective way to improve the accuracy of classification in feature selection. In this paper, experiments were performed on environmental sound dataset. An improved method based on constraint score and multimodels ensemble feature selection methods (MmEnFs) were exploited in the experiments. The experimental results show that when enough attributes are selected, the improved method can get a better performance compared to other feature selection methods. And the ensemble feature selection method, which combines other methods, can obtain the optimal performance in most cases.

scholarly journals Stability of feature selection utilizing Graph Convolutional Neural Network and Layer-wise Relevance Propagation

2021 ◽  
Author(s):  
Hryhorii Chereda ◽  
Andreas Leha ◽  
Tim Beissbarth
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Feature Selection ◽  
Deep Learning ◽  
Patient Specific ◽  
Molecular Signatures ◽  
Expression Data ◽  
Selection Methods ◽  
Feature Sets ◽  
The Stability

Motivation: High-throughput technologies play a more and more significant role in discovering prognostic molecular signatures and identifying novel drug targets. It is common to apply Machine Learning (ML) methods to classify high-dimensional gene expression data and to determine a subset of features (genes) that is important for decisions of a ML model. One feature subset of important genes corresponds to one dataset and it is essential to sustain the stability of feature sets across different datasets with the same clinical endpoint since the selected genes are candidates for prognostic biomarkers. The stability of feature selection can be improved by including information of molecular networks into ML methods. Gene expression data can be assigned to the vertices of a molecular network's graph and then classified by a Graph Convolutional Neural Network (GCNN). GCNN is a contemporary deep learning approach that can be applied to graph-structured data. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. In our recent work we developed Graph Layer-wise Relevance Propagation (GLRP) --- a method that adapts LRP to a graph convolution and explains patient-specific decisions of GCNN. GLRP delivers individual molecular signatures as patient-specific subnetworks that are parts of a molecular network representing background knowledge about biological mechanisms. GLRP gives a possibility to deliver the subset of features corresponding to a dataset as well, so that the stability of feature selection performed by GLRP can be measured and compared to that of other methods. Results: Utilizing two large breast cancer datasets, we analysed properties of feature sets selected by GLRP (GCNN+LRP) such as stability and permutation importance. We have implemented a graph convolutional layer of GCNN as a Keras layer so that the SHAP (SHapley Additive exPlanation) explanation method could be also applied to a Keras version of a GCNN model. We compare the stability of feature selection performed by GCNN+LRP to the stability of GCNN+SHAP and to other ML based feature selection methods. We conclude, that GCNN+LRP shows the highest stability among other feature selection methods including GCNN+SHAP. It was established that the permutation importance of features among GLRP subnetworks is lower than among GCNN+SHAP subnetworks, but in the context of the utilized molecular network, a GLRP subnetwork of an individual patient is on average substantially more connected (and interpretable) than a GCNN+SHAP subnetwork, which consists mainly of single vertices.

scholarly journals Optimizing the Feature Selection Process for Better Accuracy in Datasets with a Large Number of Features (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13767-13768
Author(s):  
Xi Chen ◽  
Afsaneh Doryab
Keyword(s):  
Feature Selection ◽  
Experimental Evaluation ◽  
Selection Process ◽  
High Accuracy ◽  
Frequent Pattern ◽  
Selection Methods ◽  
Feature Sets ◽  
Novel Approach ◽  
Data Points ◽  
Small Set

Most feature selection methods only perform well on datasets with relatively small set of features. In the case of large feature sets and small number of data points, almost none of the existing feature selection methods help in achieving high accuracy. This paper proposes a novel approach to optimize the feature selection process through Frequent Pattern Growth algorithm to find sets of features that appear frequently among the top features selected by the main feature selection methods. Our experimental evaluation on two datasets containing a small and very large number of features shows that our approach significantly improves the accuracy results of the dataset with a very large number of features.

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