A Bootstrap Framework for Aggregating within and between Feature Selection Methods

Salman, Reem; Alzaatreh, Ayman; Sulieman, Hana; Faisal, Shaimaa

doi:10.3390/e23020200

Cited by 15 publications

(9 citation statements)

References 27 publications

(32 reference statements)

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“…Big Data Features. In the process of effective parallel feature recognition under the big data classification of human resources, based on the result of feature optimization of unbalanced human resource data, the selective integration method is adopted to study the unbalanced database classifier in the transmission process, and the model of abnormal support degree of the transmitted unbalanced database classifier is constructed by introducing the decision profile matrix [16,17]. e support entropy is used to measure the category support degree of the classification decision matrix of the transmitted unbalanced data and to solve the fuzzy difference degree problem among each classifier set.…”

Section: Effective Parallel Recognition Methods Of Human Resourcementioning

confidence: 99%

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Wang

2021

Security and Communication Networks

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In order to solve the problem of large data classification of human resources, a new parallel classification algorithm of large data of human resources based on the Spark platform is proposed in this study. According to the spark platform, it can complete the update and distance calculation of the human resource big data clustering center and design the big data clustering process. Based on this, the K-means clustering method is introduced to mine frequent itemsets of large data and optimize the aggregation degree of similar large data. A fuzzy genetic algorithm is used to identify the balance of big data. This study adopts the selective integration method to study the unbalanced human resource database classifier in the process of transmission, introduces the decision contour matrix to construct the anomaly support model of the set of unbalanced human resource data classifier, identifies the features of the big data of human resource in parallel, repairs the relevance of the big data of human resource, introduces the improved ant colony algorithm, and finally realizes the design of the parallel classification algorithm of the big data of human resource. The experimental results show that the proposed algorithm has a low time cost, good classification effect, and ideal parallel classification rule complexity.

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Section: Effective Parallel Recognition Methods Of Human Resourcementioning

confidence: 99%

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Wang

2021

Security and Communication Networks

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“…In this research, ranking aggregation is carried out, since the output captured from both ensemble methods is based on ranking. Different types of ranking aggregation are found in practice and detailed information regarding aggregation methods is given below [22].…”

Section: Aggregation Methodologymentioning

confidence: 99%

Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

SivaSubramanian¹,

Prabha²

2022

Computer Systems Science and Engineering

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Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner. Further investigation of customer patterns helps the firm to develop efficient decisions and in turn, helps to optimize the enterprise's business and maximizes consumer satisfaction correspondingly. To conduct an effective assessment about the customers, Naive Bayes(also called Simple Bayes), a machine learning model is utilized. However, the efficacious of the simple Bayes model is utterly relying on the consumer data used, and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied. However, in practice, the NB premise is not true in consumer data, and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results. In this work, an ensemble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier. In ensemble variable selection, two different strategies are applied: one is based upon data perturbation (or homogeneous ensemble, same feature selector is applied to a different subsamples derived from the same learning set) and the other one is based upon function perturbation (or heterogeneous ensemble different feature selector is utilized to the same learning set). Furthermore, the feature set captured from both ensemble strategies is applied to NB individually and the outcome obtained is computed. Finally, the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.

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“…In light of this, methods to improve feature selection stability for medical imaging have rapidly gained attention. Several recent publications have highlighted the use of ensemble methods to improve the feature selection stability [ 16 , 17 , 18 , 19 ], mainly investigating three ensemble techniques, namely resampling, bagging, and boosting; however, their combined use has not been studied in radiomics. It is possible that using these techniques in combination could further improve the stability.…”

Section: Introductionmentioning

confidence: 99%

Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI

Wong

Zhang

et al. 2022

Cancers

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Discriminating early-stage nasopharyngeal carcinoma (NPC) from benign hyperplasia (BH) on MRI is a challenging but important task for the early detection of NPC in screening programs. Radiomics models have the potential to meet this challenge, but instability in the feature selection step may reduce their reliability. Therefore, in this study, we aim to discriminate between early-stage T1 NPC and BH on MRI using radiomics and propose a method to improve the stability of the feature selection step in the radiomics pipeline. A radiomics model was trained using data from 442 patients (221 early-stage T1 NPC and 221 with BH) scanned at 3T and tested on 213 patients (99 early-stage T1 NPC and 114 BH) scanned at 1.5T. To verify the improvement in feature selection stability, we compared our proposed ensemble technique, which uses a combination of bagging and boosting (BB-RENT), with the well-established elastic net. The proposed radiomics model achieved an area under the curve of 0.85 (95% confidence interval (CI): 0.82–0.89) and 0.80 (95% CI: 0.74–0.86) in discriminating NPC and BH in the 3T training and 1.5T testing cohort, respectively, using 17 features selected from a pool of 422 features by the proposed feature selection technique. BB-RENT showed a better feature selection stability compared to the elastic net (Jaccard index = 0.39 ± 0.14 and 0.24 ± 0.06, respectively; p < 0.001).

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A Bootstrap Framework for Aggregating within and between Feature Selection Methods

Cited by 15 publications

References 27 publications

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

Ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

Radiomics for Discrimination between Early-Stage Nasopharyngeal Carcinoma and Benign Hyperplasia with Stable Feature Selection on MRI

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