## 3 Experiment Results
### Analysis of Results
#### 3.3.1 Comparison with IEMs  
IEMs have been proven to be computationally light and have shown good performance on certain datasets [10,11]. However, IEMs only rank features based on specific criteria, and the selection of feature subsets ultimately depends on the thresholds preset by researchers. In contrast, the proposed algorithm does not require preset parameters as it automatically determines the optimal number of features. To compare the two types of FS methods, we present the average results for selecting between 5 and 40 features using IEMs. In this study, InfoGain, GainRatio, SU, ChiSquare, and Fisher are used as representative IEMs for comparison. Due to space limitations, we will only present the results of these IEMs on the dataset BPEx. However, similar results were obtained on other datasets. Figure 2 shows the average classification accuracy of the IEMs using four ML algorithms on the BPEx dataset. As seen in Figure 2, for the four ML algorithms, the classification accuracy of all the IEMs rapidly increases in the range of feature numbers between 5 and 20 and only exhibits slight changes when the feature number is greater than 20. Among the IEMs, GainRatio consistently achieves the highest classification accuracy in most cases. However, SU consistently yields significantly lower classification accuracy compared to other IEMs. To compare the IEMs with the proposed algorithm, we choose the best classification accuracy and its corresponding feature number for different IEMs and ML algorithms as the benchmark results. Table 4 displays the comparison results between the IEMs and the proposed algorithm. As shown in Table 4, the proposed algorithm outperforms the IEMs in terms of classification accuracy in all cases. Moreover, the number of features selected by MCMI is significantly smaller than that selected by the IEMs, further emphasizing the efficiency and effectiveness of the proposed algorithm. To further analyze the data quality of the features selected by the IEMs, Figure 3 shows the Rel and NRF of the IEMs on the BPEx dataset. From Figure 3a, it is apparent that for all IEMs except SU, the Rel increases rapidly within the feature number range of 5 to 15 and reaches 1 when the feature number exceeds or equals 15. By comparing Figures 2 and 3a, it can be inferred that the Rel of features plays a key role in classification accuracy and is directly proportional to the classification accuracy. Furthermore, in Figure 3b, for all IEMs except SU, the NRF increases rapidly when the feature number exceeds 15. By comparing Figures 2 and 3b, it can be inferred that redundant features can have both positive and negative effects on classification accuracy. This is because these features can introduce disturbances in ML algorithms, and the impact of this disturbance, however, is often random and irregular. For certain ML algorithms, some redundant features may enhance classification accuracy by providing additional information, while others may lead to overfitting and poor performance, especially when highly correlated with other features in the dataset. Since the IEMs cannot recognize the correlation between features, it is impossible to determine the redundant features in the selected feature subset using these methods. As a result, the proposed algorithm exhibits significant superiority over the IEMs in terms of both dimension reduction and classification accuracy, as demonstrated by the mean values presented in the last two rows of Table 4.  
Table 5 presents a comparison of execution times between the IEMs and the proposed algorithm. As observed, the execution times of the IEMs are significantly less than those of the proposed algorithm. This is because the IEMs compute only the relevance between each feature and the target class, without considering the interdependence of features. In contrast, the proposed algorithm takes into account the inter-feature dependence, making it more time-consuming.