Integrating new data balancing technique with committee networks for imbalanced data: GRSOM approach.

To deal with imbalanced data in a classification problem, this paper proposes a data balancing technique to be used in conjunction with a committee network. The proposed data balancing technique is based on the concept of the growing ring self-organizing map (GRSOM) which is an unsupervised learning algorithm. GRSOM balances the data through growing new data on a well-defined ring structure, which is iteratively developed based on the winning node nearby the samples. Accordingly, the new balanced data still preserve the topology of the original data. The performance of our proposed method is evaluated using four real data sets from the UCI Machine Learning Repository and the classification performance is measured using the fivefold cross validation method. Classifiers with most common data balancing techniques, namely the Minority Over-Sampling Technique (SMOTE) and the Random under-sampling Technique (RT), are used as the baseline methods in this study. The results reveal that a committee of classifiers constructed using GRSOM performs at least as well as the baseline methods. The results also suggest that classifiers constructed using neural networks with the backpropagation algorithm are more robust than those using the support vector machine.

Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach.

OBJECTIVE: Cholangiocarcinoma (CCA) is usually fatal because of the absence of tests for early detection and lack of effective therapy. Tumor markers with adequate diagnostic values are of clinical significance. This study is aimed to improve the diagnostic power of serum markers using the computational data mining technique to develop a combined diagnostic model that yielded the best diagnostic values for CCA. DESIGN AND METHODS: Eight CCA-associated markers-carcinoembryonic antigen, carbohydrate antigen 19-9, alkaline phosphatase (ALP), and gamma glutamyl transferase, biliary-ALP, mucin5AC, CCA-associated carbohydrate antigen (CCA-CA) and CA-S27-were used as the inputs for the C4.5 decision tree classification model and the selected model was confirmed by ANN analyses. Eight serum markers for CCA were determined in the training set of 85 histologically proven-CCA patients and 82 control subjects. The chosen set of combined markers that gave the best diagnostic values for CCA was then validated in the testing set of 22 CCA patients and 60 controls. RESULTS: A decision tree diagram built by the C4.5 algorithm suggested the serial analysis of CCA-CA and ALP for distinguishing CCA patients from non-CCA subjects with all diagnostic parameters ≥95%. The combined tests showed a precise diagnosis in the testing set. CONCLUSIONS: The C4.5 model indicates the combined markers of CCA-CA and ALP that produced the more precise diagnosis for CCA.