Novel artificial bee colony based feature selection method for filtering redundant information

Wang, Youwei; Lu, Feng; Zhu, Jianming

doi:10.1007/s10489-017-1010-4

Cited by 30 publications

(20 citation statements)

References 35 publications

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“…(2) PSO optimization is time consuming and easy to fall into local extremum, which in turn decreases the execution effectiveness of feature selection process. Moreover, most of the traditional Adaboost algorithms do not perform the feature selection process or just use the traditional feature selections (like CMFS [39], IG [40], CHI [41] and IMGI [42]) to obtain the best features [13], [16], [18], [27], deducing low classification accuracy as the influences of the changing sample weights are ignored. We now take an example to show the problem of traditional feature selections.…”

Section: B Weighted Feature Selection Methods Based On Filtersmentioning

confidence: 99%

Improved Adaboost Algorithm for Classification Based on Noise Confidence Degree and Weighted Feature Selection

Wang

2020

IEEE Access

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Adaboost is a typical ensemble learning algorithm and has been studied and widely used in classification tasks. In order to improve the classification performance of existing Adaboost algorithms effectively, a noise confidence degree and weighted feature selection based Adaboost algorithm (called NW_Ada) is proposed. Firstly, in order to decrease the impact of sample set density on noise detection results, the conceptions of clustering degree and deviated degree are introduced, and a new method of evaluating the noise confidence is proposed. Then, based on the traditional feature selections of filters, a weighted feature selection method is proposed to select the features which can effectively distinguish the samples those are misclassified. Finally, based on the traditional error rate calculation method, the category recall based classifier error rate calculation method is proposed to solve the problem that the traditional methods ignore the distribution of misclassified samples when dealing with the unbalanced datasets. The experimental results show that the proposed method comprehensively considers the influences of sample density, sample weight and dataset size on classification results, and obtains significant improvement on classification performance compared to traditional Adaboost algorithms when different datasets especially the unbalanced datasets are used.

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Section: B Weighted Feature Selection Methods Based On Filtersmentioning

confidence: 99%

Improved Adaboost Algorithm for Classification Based on Noise Confidence Degree and Weighted Feature Selection

Wang

2020

IEEE Access

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“…Kuo et al [60] combined Support Vector Machine and Decision Tree with ABC for optimizing feature selection and parameters for rule extraction Ozturk et al [61] implemented the new solution generation procedure through gene inspired components to enhance discreet ABC for handling the selection of similar cases. Wang et al [62] introduced equivalence word set to ABC for feature selection and to filter redundant information from a large pool of data. Wang et al [63] improved the initialization and scout bee steps of ABE for optimizing the classification performance and to optimize feature subset selection.…”

Section: Related Workmentioning

confidence: 99%

Ensembling Artificial Bee Colony With Analogy-Based Estimation to Improve Software Development Effort Prediction

et al. 2020

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Analogy-Based Estimation (ABE) is one of the promising estimation models used for predicting the software development effort. Researchers proposed different variants of the ABE model, but still, the most suitable procedure could not be produced for accurate estimation. In this study, an artificial Bee colony guided Analogy-Based Estimation (BABE) model is proposed which ensembles Artificial Bee Colony (ABC) with ABE for accurate estimation. ABC produces different weights, out of which the most appropriate is infused in the similarity function of ABE during the stage of model training, which are later used in the testing stage for evaluation. There are six real datasets utilized for simulating the model procedure. Five of these datasets are taken from the PROMISE repository. The predictive performance is improved for BABE over the existing ones. The most significant of its performance is found on the International Software Benchmarking Standards Group (ISBSG) dataset. INDEX TERMS Analogy based estimation, cost estimation, artificial bee colony, software development, project management.

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“…The phase space reconstruction method is used to reconstruct the characteristics of multimedia network negative information [14]. When the relative distance of multi-source text topic information distribution clustering center satisfies || ( ) ( 1) || C l C l   ﹤ , the clustering iteration of multimedia network negative information resources is obtained as: The fuzzy correlation degree characteristics of negative information resources in the multimedia network are calculated, and the expression of the detection statistical analysis model of information filtering is obtained as follows:…”

Section: Phase Space Reconstruction and Feature Extractionmentioning

confidence: 99%

“…With the rapid development of cloud storage and cloud computing technology, multimedia network negative information has become the key technology of cloud storage and database construction for the future. With the continuous expansion of the scale of data resources, a large number of cloud storage resources are distributed in the cloud integrated database system [1][2]. With the mode of cloud combination service and multimedia network negative information management, cloud storage resource sharing is realized.…”

Section: Introductionmentioning

confidence: 99%

Negative Information Filtering Algorithm based on Text Content in Multimedia Networks

Chen¹,

Fu²

2019

Int J Performability Eng

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In the multimedia network environment, it is necessary to effectively filter negative information in the multimedia network and enhance the ability to mine and identify valid data. This paper presents a new algorithm of negative information filtering based on text content in multimedia networks. The principal component features of negative information in multimedia networks are extracted, and matched filters are designed to filter the negative information reasonably. All text contents and negative information are normalized and sorted. They are transformed into the same text format for classification and processing, and the filtering and detection of negative information are realized. Finally, based on the semantic features of text content, the support vector machine algorithm is used to extract negative information features from data. Experimental results show that the algorithm improves the filtering accuracy and performance for negative information in multimedia networks, and it has good application value.

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Novel artificial bee colony based feature selection method for filtering redundant information

Cited by 30 publications

References 35 publications

Improved Adaboost Algorithm for Classification Based on Noise Confidence Degree and Weighted Feature Selection

Improved Adaboost Algorithm for Classification Based on Noise Confidence Degree and Weighted Feature Selection

Ensembling Artificial Bee Colony With Analogy-Based Estimation to Improve Software Development Effort Prediction

Negative Information Filtering Algorithm based on Text Content in Multimedia Networks

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