Determination of Vital Cancer Sites in Malaysian Colorectal Cancer Dataset by Using A Fuzzy Feature Selection Method

Abstract In Malaysia, Colorectal Cancer (CRC) is one of the most common cancers that occur in both men and women. Early detection is very crucial and it can significantly increase the rate of survival for the patients and if left untreated can lead to death. With the lack of high-quality CRC data, expert systems and machine learning analysis are burdened with the presence of irrelevant features, outliers, and noise. This can reduce the classification accuracy for data analysis. Accordingly, it is essential to find a reliable feature selection method that can identify and remove any irrelevant feature while being resistant to noise and outliers. In this paper, Fuzzy Principal Component Analysis (FPCA) was tested for the classification of Malaysian’s CRC dataset. With the utilization of fuzzy membership in FPCA, the experimental results showed that the proposed method produces higher accuracy compared to PCA and SVM by almost 2% and 5% respectively. Empirical results showed that FPCA is a reliable feature selection method that can find the most informative features in the CRC dataset that could assist medical practitioners in making an informed decision.

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A Novel Feature Selection Method Based on Principal Component Analysis for Network Traffic Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.4510 ◽

2014 ◽

Vol 989-994 ◽

pp. 4510-4513

Author(s):

Hong Zhi Wang ◽

Jian Ping Zhang ◽

Zun Yi Shang

Keyword(s):

Feature Selection ◽

Network Traffic ◽

Feature Selection Method ◽

Principal Component ◽

Selection Method ◽

Performance Comparison ◽

Traffic Classification ◽

Network Traffic Classification ◽

Validation Experiment ◽

Pca Method

In network traffic classification, by conventional PCA method, more features still exist due to uniform contribution rates for most of features. To overcome this problem, in this paper, a novel feature selection method is proposed to reduce data dimension of network traffic. A contribution rate of various features in each component is calculated by a new weight criterion. A maxima-order principle is proposed to determine feature selection. Based on three multi-class classification methods, performance comparison is conducted by actual traffic data with 10-fold cross-validation. Experiment shows that the proposed method has higher classification accuracy than conventional PCA method.

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A Novel Feature Selection Method for Process Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.2045 ◽

2013 ◽

Vol 427-429 ◽

pp. 2045-2049

Author(s):

Chun Mei Yu ◽

Sheng Bo Yang

Keyword(s):

Feature Selection ◽

Fault Diagnosis ◽

Sparse Representation ◽

Selection Process ◽

Feature Selection Method ◽

Principal Component ◽

Selection Method ◽

Fault Classification ◽

Process Data ◽

Bhattacharyya Distance

To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

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A hybrid feature selection method based on fuzzy feature selection and consistency measures

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems ◽

10.1109/icicisys.2009.5358395 ◽

2009 ◽

Cited By ~ 1

Author(s):

Laleh Jalali ◽

Mahdi Nasiri ◽

Behrooz Minaei

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Fuzzy Feature Selection

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Coordinating Computational and Visual Approaches for Interactive Feature Selection and Multivariate Clustering

Information Visualization ◽

10.1057/palgrave.ivs.9500053 ◽

2003 ◽

Vol 2 (4) ◽

pp. 232-246 ◽

Cited By ~ 62

Author(s):

Diansheng Guo

Keyword(s):

Feature Selection ◽

High Dimensional Data ◽

Feature Selection Method ◽

Selection Method ◽

High Dimensional ◽

Cancer Dataset ◽

Data Space ◽

Exploration Environment ◽

Interactive Feature ◽

High Dimensional Datasets

Unknown (and unexpected) multivariate patterns lurking in high-dimensional datasets are often very hard to find. This paper describes a human-centered exploration environment, which incorporates a coordinated suite of computational and visualization methods to explore high-dimensional data for uncovering patterns in multivariate spaces. Specifically, it includes: (1) an interactive feature selection method for identifying potentially interesting, multidimensional subspaces from a high-dimensional data space, (2) an interactive, hierarchical clustering method for searching multivariate clusters of arbitrary shape, and (3) a suite of coordinated visualization and computational components centered around the above two methods to facilitate a human-led exploration. The implemented system is used to analyze a cancer dataset and shows that it is efficient and effective for discovering unknown and unexpected multivariate patterns from high-dimensional data.

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