Deep Learning Approach for Knee Point Detection on Noisy Data

TL;DR

This paper introduces a deep learning method using CNNs to accurately detect knee points in noisy, normalized data, outperforming existing techniques and providing a new benchmark dataset for evaluation.

Contribution

It proposes a novel CNN-based approach with a U-Net architecture for knee point detection in noisy data and creates synthetic datasets for robust evaluation.

Findings

The proposed CNN model outperforms state-of-the-art methods in synthetic datasets.

Normalization affects curvature and knee point detection, which is analyzed.

Synthetic data with noise effectively simulates real-world scenarios for benchmarking.

Abstract

A knee point on a curve is the one where the curve levels off after an increase. In a computer system, it marks the point at which the system's performance is no longer improving significantly despite adding extra resources. Thus a knee point often represents an optimal point for decision. However, identifying knee points in noisy data is a challenging task. All previous works defined knee points based on the data in the original scale. However, in this work, we define knee points based on normalized data and provide a mathematical definition of curvature for normalized discrete data points, based on the mathematical definition of curvature for continuous functions. The impact of normalization exerted on curvature and the location of knee points are also discussed. Nevertheless, assessing the effectiveness of methods is difficult in the absence of ground truth data and benchmark datasets, which makes comparing existing methods challenging. In view of this, we create synthetic data that simulate real-world scenarios. We achieve this by selecting a set of functions that possess the required characteristics in this research and then introducing noise that satisfies the underlying distribution. In addition, we present a deep-learning approach and employ a Convolutional Neural Network (CNN) with a U-Net-like architecture, to accurately detect the knee point(s) of the underlying true distribution. The proposed model is evaluated against state-of-the-art methods. Experiments show that our network outperforms existing methods in all synthetic datasets, regardless of whether the samples have single or multiple knee points. In fact, our model achieves the best $F_{1}$ scores among all existing methods in all the test sets.