Data Mining for Bioinformatics: Design with Oversampling and Performance Evaluation

Research into cancer prediction has applied various machine learning algorithms, such as neural networks, genetic algorithms, and particle swarm optimization, to find the key to classifying illness or cancer properties or to adapt traditional statistical prediction models to effectively differentiate between different types of cancers, and thus build prediction models that can allow for early detection and treatment. Training data from existing patients is used to establish models to predict the classification accuracy of new patient samples. This issue has attracted considerable attention in the field of data mining, and scholars have proposed various methods (e.g., random sampling and feature selection) to address category imbalances and achieve a re-balanced class distribution, thus improving the effectiveness of classifiers with limited data. Although resampling methods can quickly deal with the problem of unbalanced samples, they give more importance to the data in the majority class, and neglect potentially important data in the minority class, thus limiting the effectiveness of classification. Based on patterns discovered in imbalanced medical data sets, this research uses the synthetic minority oversampling technique to improve imbalanced data set issues. In addition, this research also compares the resampling performance of various methods based on machine learning, soft-computing, and bio-inspired computing, using three UCI medical data sets.