Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification

Abstract

Machine Learning has attracted considerable attention throughout the past decade due to its potential to solve far-reaching tasks, such as image classification, object recognition, anomaly detection, and data forecasting. A standard approach to tackle such applications is based on supervised learning, which is assisted by large sets of labeled data and is conducted by the so-called classifiers, such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines, among others. An alternative to traditional classifiers is the parameterless Optimum-Path Forest (OPF), which uses a graph-based methodology and a distance measure to create arcs between nodes and hence sets of trees, responsible for conquering the nodes, defining their labels, and shaping the forests. Nevertheless, its performance is strongly associated with an appropriate distance measure, which may vary according to the dataset's nature. Therefore, this work proposes a comparative study over a wide range of distance measures applied to the supervised Optimum-Path Forest classification. The experimental results are conducted using well-known literature datasets and compared across benchmarking classifiers, illustrating OPF's ability to adapt to distinct domains.

An Intrusion Detection Method for Industrial Control System Based on Machine Learning

“…In this sectio propose the normal_DIFF_RF-OPFYTHON detection model. It uses the binary clas tion normal_DIFF_RF [20] module to filter the attack traffic in the detected network firstly, and then uses the multi-classification OPFYTHON [21] module to classi…”

“…In this section, we propose the normal_DIFF_RF-OPFYTHON detection model. It uses the binary classification normal_DIFF_RF [20] module to filter the attack traffic in the detected network traffic firstly, and then uses the multi-classification OPFYTHON [21] module to classify the attack traffic. In this way, the OPFYTHON module does not need to fit a large of normal traffic during the training phase, and solves the imbalance of datasets caused by a large of normal network traffic.…”