# On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction
###### Abstract  
Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some related studies have considered feature selection to remove irrelevant features from the datasets for further performance improvement. However, since the order of combining feature selection and over-sampling can result in different training sets to construct the prediction model, it is unknown which order performs better. In this paper, the information gain (IG) and genetic algorithm (GA) feature selection methods and the synthetic minority over-sampling technique (SMOTE) are used for different combinations. The experimental results based on two breast cancer datasets show that the combination of feature selection and over-sampling outperform the single usage of either feature selection and over-sampling for the highly class imbalanced datasets. In particular, performing IG first and SMOTE second is the better choice. For other datasets with a small class imbalance ratio and a smaller number of features, performing SMOTE is enough to construct an effective prediction model. breast cancer; data mining; machine learning; feature selection; over-sampling; class imbalance +
Footnote †: journal: Medical  
for re-balancing class imbalanced datasets in order to decrease the effect of skewed class distribution in the learning process [6; 7]. For related works of feature selection, [PERSON] et al. [8] propose a novel feature selection method based on the genetic algorithm to select a gene subset from high dimensional gene data, which causes different classifiers perform better than the ones without feature selection. In [9], a genetic algorithm is used for feature selection, where the selected subset is used to construct different classifiers for performance comparisons. On the other hand, [PERSON] and [PERSON] [10] use a gradient boosting decision tree with Bayesian optimization to remove the irrelevant and redundant features from gene expression data. [PERSON] et al. [11] compare several feature selection methods to determine the best one to combine with the random forest classifier. For related works on class imbalance learning, [PERSON] et al. [12] propose a clustering-based under-sampling method to select informative samples from the clusters identified in the majority and minority classes, and the decision tree based on this boosting technique is employed for the prediction model. In [13], eighteen different under- and over-sampling methods are used to balance related class imbalanced cancer datasets, in which the over-sampling methods perform better than the under-sampling ones. [PERSON] et al. [14] apply the synthetic minority over-sampling technique (SMOTE) to balance the training dataset and employ the stacking ensemble method to combine multiple classifiers, which achieved better performance than conventional methods. [PERSON] et al. [15] investigated the effect of performing SMOTE on five different classifiers to determine the best one for breast cancer prediction. According to [PERSON] et al. [16], SMOTE over-sampling can benefit from the use of feature selection, where feature selection is performed over the class imbalanced dataset to select a subset feature of it, and then the reduced dataset is over-sampled to make it contain the same size of the data samples as in the majority and minority classes. Recently, [PERSON] et al. [17] propose the contrary procedure that SMOTE be performed first to re-balance the breast cancer dataset, and then wrapper-based feature selection methods can be applied to reduce the feature dimensions. However, to the best of our knowledge there is not any study examining the performances of both procedures to combine feature selection and over-sampling for breast cancer prediction.