Artif Intell Med - A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

Tópicos

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Resumo

JECTIVE: Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small.METHODS: This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper.RESULTS: This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance.CONCLUSION: This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches.

Resumo Limpo

jectiv medic data set usual small high dimension mani attribut will make analysi less effici will necessarili increas accuraci data will decreas model stabil consequ main object studi extract optim subset featur increas analyt perform data set smallmethod paper propos fuzzybas nonlinear transform method extend classif relat inform origin data attribut valu small data set base new transform data set studi appli princip compon analysi pca extract optim subset featur final use transform data optim featur input data learn tool support vector machin svm six medic data set pima indian diabet wisconsin diagnost breast cancer parkinson diseas echocardiogram bupa liver disord dataset bladder cancer case taiwan employ illustr approach present paperresult research use ttest evalu classif accuraci singl data set use friedman test show propos method better method multipl data set experi result indic propos method better classif perform either pca kernel princip compon analysi kpca data set small suggest creat new purposerel inform improv analysi performanceconclus paper shown featur extract import function featur select effici data analysi data set small use fuzzybas transform method present work increas inform avail produc better result pca kpca approach

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