↓ Skip to main content

Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications

Overview of attention for article published in BioMedical Engineering OnLine, November 2017
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
60 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications
Published in
BioMedical Engineering OnLine, November 2017
DOI 10.1186/s12938-017-0416-x
Pubmed ID
Authors

Yiyan Zhang, Yi Xin, Qin Li, Jianshe Ma, Shuai Li, Xiaodan Lv, Weiqi Lv

Abstract

Various kinds of data mining algorithms are continuously raised with the development of related disciplines. The applicable scopes and their performances of these algorithms are different. Hence, finding a suitable algorithm for a dataset is becoming an important emphasis for biomedical researchers to solve practical problems promptly. In this paper, seven kinds of sophisticated active algorithms, namely, C4.5, support vector machine, AdaBoost, k-nearest neighbor, naïve Bayes, random forest, and logistic regression, were selected as the research objects. The seven algorithms were applied to the 12 top-click UCI public datasets with the task of classification, and their performances were compared through induction and analysis. The sample size, number of attributes, number of missing values, and the sample size of each class, correlation coefficients between variables, class entropy of task variable, and the ratio of the sample size of the largest class to the least class were calculated to character the 12 research datasets. The two ensemble algorithms reach high accuracy of classification on most datasets. Moreover, random forest performs better than AdaBoost on the unbalanced dataset of the multi-class task. Simple algorithms, such as the naïve Bayes and logistic regression model are suitable for a small dataset with high correlation between the task and other non-task attribute variables. K-nearest neighbor and C4.5 decision tree algorithms perform well on binary- and multi-class task datasets. Support vector machine is more adept on the balanced small dataset of the binary-class task. No algorithm can maintain the best performance in all datasets. The applicability of the seven data mining algorithms on the datasets with different characteristics was summarized to provide a reference for biomedical researchers or beginners in different fields.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 15%
Student > Bachelor 8 13%
Student > Master 7 12%
Student > Ph. D. Student 6 10%
Student > Doctoral Student 6 10%
Other 12 20%
Unknown 12 20%
Readers by discipline Count As %
Computer Science 25 42%
Engineering 15 25%
Mathematics 2 3%
Unspecified 1 2%
Social Sciences 1 2%
Other 1 2%
Unknown 15 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 November 2017.
All research outputs
#15,963,539
of 19,876,564 outputs
Outputs from BioMedical Engineering OnLine
#513
of 766 outputs
Outputs of similar age
#249,840
of 332,976 outputs
Outputs of similar age from BioMedical Engineering OnLine
#26
of 41 outputs
Altmetric has tracked 19,876,564 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 766 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,976 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.