A CNN based framework for stable image feature selection

Han, Chaoyi; Tao, Xiaoming; Duan, Yiping; Liu, Xijia; Lu, Jianhua

doi:10.1109/globalsip.2017.8309192

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computerized Tomography Scan (CT-Scans), for instance, generate a large number of images for each patient. The physician's decision depends heavily upon analyzing these images and other related results [9]. However, it is time-consuming and difficult to identify diseases in a timely manner, especially, in case of early disease.…”

mentioning

confidence: 99%

Stable bagging feature selection on medical data

Alelyani

2021

J Big Data

View full text Add to dashboard Cite

In the medical field, distinguishing genes that are relevant to a specific disease, let’s say colon cancer, is crucial to finding a cure and understanding its causes and subsequent complications. Usually, medical datasets are comprised of immensely complex dimensions with considerably small sample size. Thus, for domain experts, such as biologists, the task of identifying these genes have become a very challenging one, to say the least. Feature selection is a technique that aims to select these genes, or features in machine learning field with respect to the disease. However, learning from a medical dataset to identify relevant features suffers from the curse-of-dimensionality. Due to a large number of features with a small sample size, the selection usually returns a different subset each time a new sample is introduced into the dataset. This selection instability is intrinsically related to data variance. We assume that reducing data variance improves selection stability. In this paper, we propose an ensemble approach based on the bagging technique to improve feature selection stability in medical datasets via data variance reduction. We conducted an experiment using four microarray datasets each of which suffers from high dimensionality and relatively small sample size. On each dataset, we applied five well-known feature selection algorithms to select varying number of features. The proposed technique shows a significant improvement in selection stability while at least maintaining the classification accuracy. The stability improvement ranges from 20 to 50 percent in all cases. This implies that the likelihood of selecting the same features increased 20 to 50 percent more. This is accompanied with the increase of classification accuracy in most cases, which signifies the stated results of stability.

show abstract

mentioning

confidence: 99%