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An Ensemble Feature Selection Framework Integrating Stability

Xiaokang Zhang, Inge Jonassen
2019 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
We therefore propose a framework, EFSIS (Ensemble Feature Selection Framework Integrating Stability), combining these two strategies and integrating stability during the aggregation of selectors.  ...  Ensemble feature selection has drawn more and more attention in recent years. There are mainly two strategies for ensemble feature selection, namely data perturbation and function perturbation.  ...  The framework is named EFSIS (Ensemble Feature Selection Integrating Stability) and the source code is available on GitHub (https://github.com/zhxiaokang/EFSIS).  ... 
doi:10.1109/bibm47256.2019.8983310 dblp:conf/bibm/ZhangJ19 fatcat:qu2goqukrja2zg76ly7yg4xmfm

EFSIS: Ensemble Feature Selection Integrating Stability [article]

Xiaokang Zhang, Inge Jonassen
2018 arXiv   pre-print
This ensemble logic has recently also been more applied in feature selection. There are basically two strategies for ensemble feature selection, namely data perturbation and function perturbation.  ...  Here we propose a framework, EFSIS, combining these two strategies. Empirical results indicate that EFSIS gives both high prediction accuracy and stability.  ...  For this purpose, we propose the EFSIS (Ensemble Feature Selection Integrating Stability) framework combining both approaches and using the stability of each feature selection method to perform a weighted  ... 
arXiv:1811.07939v1 fatcat:zcic4h2btzbf7diedqr6nmhnty

Framework for the Ensemble of Feature Selection Methods

Maritza Mera-Gaona, Diego M. López, Rubiel Vargas-Canas, Ursula Neumann
2021 Applied Sciences  
of features selected by the ensemble feature selection framework.  ...  Thus, methods for ensemble feature selection (EFS) algorithms have become an alternative to integrate the advantages of single FS algorithms and compensate for their disadvantages.  ...  The conceptual framework built allowed the authors to guide the development of an implementation framework capable of selecting features using an ensemble of FS methods.  ... 
doi:10.3390/app11178122 fatcat:y4mtz54wbncdnn67lse2eew7em

The Core Cluster-Based Subspace Weighted Clustering Ensemble

Xuan Huang, Fang Qin, Lin Lin
2022 Wireless Communications and Mobile Computing  
The proposed framework first combines random feature selection and unsupervised feature selection to generate a set of base subspaces.  ...  weighted clustering ensemble framework for high-dimensional data.  ...  The proposed framework first uses a combination of random feature selection and unsupervised feature selection to generate a set of base subspaces.  ... 
doi:10.1155/2022/7990969 doaj:660a63111d2b4032a799844fd48cec78 fatcat:epz4z2wf5vafnprqacqmew2skq

Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading

Mingdong Zhang, Li Chen, Quan Li, Xiaoru Yuan, Junhai Yong
2020 IEEE Transactions on Visualization and Computer Graphics  
Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data.  ...  As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines.  ...  VISUALIZATION DESIGN Based on the proposed feature spreading technique, we build an interactive visualization framework (Fig. 1) , which consists of (A) a parameter setting panel, (B) a region stability  ... 
doi:10.1109/tvcg.2020.3030377 pmid:33048703 fatcat:7xtesv4oy5bb3bm2mgmuwfvuoi

Integration Of Multi-Omics Data For Prediction Of Metabolic Traits

Jelena Čuklina, Yibo Wu, Evan. G. Williams, María Rodríguez-Martínez, Ruedi Aebersold
2016 Zenodo  
The aim is to develop a framework for selection of a composite biomarker: an ensemble of small number of predictors, that is able to predict the macro-level response.  ...  Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error.  ...  Develop a framework for selection of a composite biomarker: an ensemble of small number of predictors, that is able to predict the macro-level response.  ... 
doi:10.5281/zenodo.846702 fatcat:psnbnmpxjrac5eyfuprdluy7oi

Ensembling improves stability and power of feature selection for deep learning models [article]

Prashnna K Gyawali, Xiaoxia Liu, James Zou, Zihuai He
2022 arXiv   pre-print
an ensemble of feature importance scores from numerous good models.  ...  As such, we present a framework to combine the feature importance of trained models across different hyperparameter settings and epochs, and instead of selecting features from one best model, we perform  ...  Ensemble framework to improve stability and power of feature selection In this section, we present the framework based on feature importance ensembling to improve the stability and power of feature selection  ... 
arXiv:2210.00604v1 fatcat:d3ck62k2hjgobaza3jrvow2ybe

Ensemble Feature Weighting Based on Local Learning and Diversity

Yun Li, Suyan Gao, Songcan Chen
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection.  ...  Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention.  ...  an unstable subset may be reduced by ensemble feature selection.  ... 
doi:10.1609/aaai.v26i1.8275 fatcat:2dmzmiry4bec5ks7gia53lwd3e

Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model

Guofeng Wang, Yinwei Yang, Zhimeng Li
2014 Sensors  
relevance (mRMR) algorithm is utilized to select the most prominent features.  ...  Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal  ...  The remainder of the paper is organized as follows: in Section 2, a heterogeneous ensemble learning framework is presented.  ... 
doi:10.3390/s141121588 pmid:25405514 pmcid:PMC4279551 fatcat:ugefulvjjvd3jhqdnwxz52xyai

A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction

Qingwen Li, Chengming Yu, Guangxi Yan
2022 IEEE Access  
To build an accurate GDP prediction model, this paper proposed a new multi-predictor ensemble decision framework based on deep reinforcement learning.  ...  Then, the DQN algorithm effectively analyses the adaptability of these three neural networks to different GDP datasets to obtain an ensemble model.  ...  The stability of the model is proved and the optimal parameters are selected. (4) Table 9 shows the selection results of different neural network input features.  ... 
doi:10.1109/access.2022.3170905 fatcat:qb2en2b5hbfnfdfpg77pgnu6si

Ensemble Learning for Power Systems TTC Prediction with Wind Farms

Gao Qiu, Junyong Liu, Youbo Liu, Tingjian Liu, Gang Mu
2019 IEEE Access  
INDEX TERMS Artificial neural networks, ensemble learning, feature selection, total transfer capability, wind power.  ...  The results illustrate that combining with the appropriate feature selection, the presented ensemble learning has high performance on creating the accurate TTC predictor, which enables online secure margin  ...  Therefore, in order to enable accurate and real-time awareness, by integrating DPM-RDPF samples generation, MIC & NIS feature selection, and AHGA-NNs training, this paper presented an ensemble learning  ... 
doi:10.1109/access.2019.2896198 fatcat:xciczmu6jnbypcrebooe45tmmm

Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

Zhice Fang, Yi Wang, Gonghao Duan, Ling Peng
2021 Remote Sensing  
This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique.  ...  The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations.  ...  Methodology The proposed framework is based on the integration of DTs and the RF ensemble technique.  ... 
doi:10.3390/rs13020238 fatcat:cpjhn6tgafgxzeuso2s7hjwjr4

Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach

Adel Binbusayyis, Thavavel Vaiyapuri
2019 IEEE Access  
INDEX TERMS Anomaly intrusion detection, correlation, consistency, data analytic lifecycle, diversity measure, ensemble learning, feature selection, information gain, ReliefF, stability measure.  ...  Along with this, a new framework that adopts the data analytic lifecycle practices is explored to employ the proposed ensemble for building an effective IDS.  ...  The stability among the ensemble members have also contributed in achieving an stability measure of 0.8 in the proposed ensemble approach. B.  ... 
doi:10.1109/access.2019.2929487 fatcat:25t6wa6ikbhb5mg72qaryejpwu

The Ensembl genome database project

T. Hubbard
2002 Nucleic Acids Research  
The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes.  ...  an interactive web site or as flat files.  ...  The Ensembl project is principally funded by the Wellcome Trust with additional funding from EMBL.  ... 
doi:10.1093/nar/30.1.38 pmid:11752248 pmcid:PMC99161 fatcat:u2fj5iq6tfga7kj7xrc3bk3su4

A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges [article]

Shitu Zhang, Zhixun Zhu, Yang Li
2021 arXiv   pre-print
TSA an increasingly urgent task.  ...  This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and  ...  Reference [24] proposes an mRMR-based mutual information criterion for feature selection.  ... 
arXiv:2111.00978v1 fatcat:byrrmsopbfdnxjghsw4vn7p4im
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