# FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks

## TL;DR

The paper introduces FG-SSA, a low-complexity algorithm that efficiently identifies and removes nonimportant signals from CNN inputs, reducing computational costs while maintaining classification accuracy.

## Contribution

FG-SSA is a novel linear-time algorithm that leverages features gradient via grad-CAM to select important signals for CNN classification.

## Key findings

- Removed an average of 6.55 signals without losing accuracy
- Achieved low computational complexity of O(N)
- Validated on OPPORTUNITY dataset with high generalization scores

## Abstract

Recently, many convolutional neural networks (CNNs) for classification by time domain data of multisignals have been developed. Although some signals are important for correct classification, others are not. When data that do not include important signals for classification are taken as the CNN input layer, the calculation, memory, and data collection costs increase. Therefore, identifying and eliminating nonimportant signals from the input layer are important. In this study, we proposed features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing nonimportant signals for classification by utilizing features gradient obtained by the calculation process of grad-CAM. When we define N as the number of signals, the computational complexity of the proposed algorithm is linear time O(N), that is, it has a low calculation cost. We verified the effectiveness of the algorithm using the OPPORTUNITY Activity Recognition dataset, which is an open dataset comprising acceleration signals of human activities. In addition, we checked the average 6.55 signals from a total of 15 acceleration signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, the proposed algorithm FG-SSA has an effect on finding and removing signals that are not important for CNN-based classification.