A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making

Lee, Shin-Jye; Xu, Zhaozhao; Li, Tong; Yang, Yun

doi:10.1016/j.jbi.2017.11.005

Cited by 81 publications

(43 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Akintola et al [18] performed a comparative analysis of classifiers based on FFS on SDP and their results gave credit to the usage of FFS, but there can still be further analysis using other FS methods. It has been proven empirically that wrappers obtain subsets with better performance than filter feature selection because the subsets were evaluated using a real modeling algorithm [33,34]. Rodriguez et al [35] have also conducted comparative experiments on FS methods based on three different FFR and WRP models on four software defect datasets.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

et al. 2019

View full text Add to dashboard Cite

Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Akintola et al [9] played out an analysis of classifiers dependent on FFS on SDP and their outcomes offered credit to the use of FFS, yet there can even now be further examination utilizing different FS strategies. It has been demonstrated exactly that wrappers acquire subsets with preferable execution over channel include determination in light of the fact that the subsets were assessed utilizing a genuine modeling algorithm [10,11]. Rodriguez et al [12] have likewise focused relative analyses on FS techniques dependent on three different FFR and WRP models on four programming defect datasets.…”

Section: Review Of Literaturementioning

confidence: 99%

Svs: Prediction Framework for Software Quality Enhancement through Data Mining Techniques

Visagan¹,

Sumathi²,

Sujatha³

2019

IJEAT

View full text Add to dashboard Cite

Software Engineering has its origins in tackling the issue of development and maintenance of quality software. Software Quality has been defined in multiple ways but the broadest definition is that quality is the extent to which the customer is satisfied with the developed software. Data mining has the prospects of being applied to multiple domains and addressing the long standing issues faced by them. It has been successfully applied to uncover solutions to complex problems that have long confronted these domains. The proposed research is a step in the direction. It will attempt to apply existing data mining algorithms to data accumulated by software organizations in an attempt to extract useful patterns that can go a long way in addressing the issue of software quality. This work proposed Spacious Virtue Suggestion (SVS) Model for analyzing code based quality in software quality model. The first layer of this model is Extraction Layer that extracts the various attributes of software code used. After the extraction of the metrics attributes are constructed as a vector is considered as the feature vector for the second layer of the SVS Model. The second layer of the SVS model is Selection Layer which employs feature selection strategy to obtain significant metrics attributes for the software quality prediction by reducing the overlapping metrics attributes from the vector... The third layer of SVS Model is Prediction Layer which predict the good class from the training set and result shows the high accuracy in the proposed system.

show abstract

“…Machine learning has shown great success in variety of application fields, including computer vision, object recognition, and natural language processing [1], [2]. Some scholars have applied machine learning in the medical field, which led to the emergence of machine learning-driven intelligent auxiliary diagnostic systems [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Transfer Learning via Ensemble Approach for Initial Diagnosis of Alzheimer’s Disease

Yang

Wang

et al. 2020

IEEE J. Transl. Eng. Health Med.

Self Cite

View full text Add to dashboard Cite

Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases, and the number of AD patients has increased year after year with the global aging trend. The onset of AD has a long preclinical stage. If doctors can make an initial diagnosis in the mild cognitive impairment (MCI) stage, it is possible to identify and screen those at a high-risk of developing full-blown AD, and thus the number of new AD patients can be reduced. However, there are problems with the medical datasets including AD data, such as insufficient number of samples and different data distributions. Transfer learning, which can effectively solve the problem of distribution discrepancy between training and test data and an insufficient number of target samples, has attracted increasing attention over recent years. In this paper, we propose a multi-source ensemble transfer learning (METL) approach by introducing ensemble learning and our tritransfer model that uses Tri-Training, which ensures the transferability of source data by the tri-transfer model and high performance through ensemble learning. The experimental results on the benchmark and AD datasets demonstrate that our proposed approach has effective transferability, robustness, and feasibility, and is superior to existing algorithms. Based on METL, we propose an auxiliary diagnosis system for the initial diagnosis of AD, which helps doctors identify patients in the MCI stage as quickly as possible and with high accuracy so that measures can be taken to prevent or delay the occurrence of AD.

show abstract

A novel bagging C4.5 algorithm based on wrapper feature selection for supporting wise clinical decision making

Cited by 81 publications

References 60 publications

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

Svs: Prediction Framework for Software Quality Enhancement through Data Mining Techniques

Multi-Source Transfer Learning via Ensemble Approach for Initial Diagnosis of Alzheimer’s Disease

Contact Info

Product

Resources

About