## 4 Experiments
### Research Question and Results
The main reason we choose this method is the convenience for selecting certain number of features in the experiment, and it removes features by univariate analyze suits for datasets. Results: The results are shown in Tables 14 and 15. We notice that the average balancedscore values vary from 0.636 to 0.662 and the average G-mean from 0.534 to 0.631. When the number of feature is 16, the values of average balancedscore and G-mean are highest. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Metrics** & **Total** & **LIMCR** & **SMOE** & **IHT** \\ \hline Balancedscore & 0.024 & 0.011 & 0.165 & 0.051 \\ G-mean & 0.123 & 0.085 & 0.154 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: \(p\)-Value of Friedman test between different classifiers for resampling methods. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **4** & **8** & **12** & **16** & **20** \\ \hline synapse-1.2 & 0.642 & 0.649 & 0.638 & 0.659 & 0.68 \\ jedit-3.2 & 0.659 & 0.738 & 0.762 & 0.741 & 0.772 \\ synapse-1.1 & 0.637 & 0.661 & 0.661 & 0.623 & 0.622 \\ log4j-1.0 & 0.643 & 0.649 & 0.673 & 0.542 & 0.577 \\ jedit-4.0 & 0.571 & 0.571 & 0.579 & 0.621 & 0.657 \\ ant-1.7 & 0.716 & 0.768 & 0.773 & 0.761 & 0.767 \\ camel-1.6 & 0.531 & 0.539 & 0.562 & 0.569 & 0.568 \\ camel-1.4 & 0.518 & 0.564 & 0.587 & 0.622 & 0.63 \\ Xerces-1.3 & 0.607 & 0.609 & 0.692 & 0.674 & 0.675 \\ Xalan-2.4 & 0.588 & 0.589 & 0.619 & 0.686 & 0.661 \\ jedit-4.2 & 0.616 & 0.639 & 0.685 & 0.683 & 0.726 \\ arc & 0.699 & 0.693 & 0.646 & 0.631 & 0.59 \\ synapse-1.0 & 0.922 & 0.822 & 0.853 & 0.707 & 0.707 \\ tomcat & 0.614 & 0.621 & 0.64 & 0.666 & 0.704 \\ camel-1.0 & 0.576 & 0.537 & 0.523 & 0.745 & 0.559 \\ \hline Average & 0.636 & 0.643 & 0.66 & **0.662** & 0.66 \\ Rank & 3.9 & 3.333 & 2.567 & 2.767 & **2.433** \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Balancedscore of different number features in GNB with LIMCR on small datasets. From the \(p\)-value of Friedman test in Table 16 we know for metric balancedscore there is no significant difference between the number of features in five levels. According to the result of [PERSON] post hoc test (CD = 1.575), the Friedman average ranks which larger than the best one more than 1.575. For metric G-mean, only datasets with four features perform significantly worse the best one and others has no significant difference. When the number of features declines, the precision score increases and the overall performance is declining. In summary, it can be believed that the number of features has no influence on the performance of LIMCR unless the number of features below a certain value. The value in this experiment is 4 or 5. RQ5: How does LIMCR work with datasets with large sample sizes? Motivation: From RQ1 we know sample size has a great influence on classifier selecting in imbalance learning and our proposed LIMCR is proved performing well with small datasets. However, how LIMCR performs on datasets with large sample size is also needed to know. Approach: In this experiment we combine LIMCR with three classifiers, GNB, ABC and DTC. IHT combined with the same classifiers are used as comparison. The datasets are large datasets introduced in RQ1. Results: The results of comparison between six combined methods are listed in Tables 17 and 18.