## 3 Method
### Proposed Method
By adding the CancelOut layer, the value after the activation function of the CancelOut layer indicates the contribution to the output of the corresponding variable, and important variables can be extracted through the trained weight. After the CancelOut layer, the output of the CancelOut layer is reshaped to (210 \(\times\) 1) to be used as the input of the convolutional layer:  
\[\textit{CancelOut}(\textbf{X})=\textbf{X}\otimes\sigma(\textbf{W}_{\textit{ CancelOut}}) \tag{1}\]  
\[\textbf{W}_{\textit{CancelOut}}\ \sim\ \textbf{u}\left(-\frac{\textbf{1}}{\sqrt{n_{ \textbf{X}}}}+\beta,\frac{\textbf{1}}{\sqrt{n_{\textbf{X}}}}+\beta\right) \tag{2}\]  
The feature extractor of the CNN extracts features from many variables of the input data. In general, the feature extractor step is divided into a convolutional layer for extracting features and a pooling layer for sub-sampling the extracted features. In particular, the convolutional layer extracts useful features from the input using filters and activation functions. The filter is moved in the height direction using a 1D convolutional layer, which is suitable for the dataset of this study because it expresses local features well regardless of location. However, because our dataset has 210 input dimensions, information could be lost in the process of feature extraction. To solve this problem, we used three filters of  
Figure 4: The proposed explainable multiple-filter CNN Architecture (XmCNN). different sizes, rather than a single filter. Multiple filters have been used in natural language processing and computer vision, and it has been demonstrated that convolutional layers applying multiple filters and feature maps can have greater capacity [29]. In particular, using multiple filters has the advantage that different kernels can detect various features of a local region [30], and the model performance is improved compared to using only a single filter. The filter window size of the 1D convolutional layer determines the amount of context information to be extracted from the variable. In this study, we use the window size of each filter as [3,4,5] and extract multiple features from multiple filters. Three 1D convolutional layers with different filter sizes accept input data and extract individual feature maps. Because the number of feature maps is determined by the number of filters, the proposed model compresses features by reducing the number of filters from 32 to 16 and then 8. In addition, padding is applied before convolution. The \"same\" padding is applied to ensure that the output of the convolution operation has the same length as the original input. Padding refers to the addition of zero values to the edges of the input image matrix to prevent the output values from becoming smaller and lost. Additionally, the dropout layer is placed after the last convolutional layer. Dropout is a regularization method, and most CNN models use dropout to prevent overfitting. After the dropout layer, the proposed model includes an average pooling layer added to the subsample of the extracted features. Average pooling has the advantage of obtaining invariance, which is advantageous for classification and can reduce the CNN feature dimensions by summarizing spatial information. The process from convolutional layer to average pooling is called a conv-block, and our model consists of three conv-blocks with different filter sizes. Subsequently, features extracted from the three conv-blocks are concatenated as integrated features. This process is similar to the inception module [31] that concatenates the results of each filter, and features extracted from various local regions are combined. The concatenated feature is transferred to the input of the classifier part, as shown in Figure 4. The classifier in the CNN calculates the probability value of the target label. The classifier consists of five fully connected layers, a dropout layer, and an output layer that calculates the probability values.