Tracing outliers in the dataset of Drosophila suzukii records with the Isolation Forest method

Santosuosso, Ugo; Cini, Alessandro; Papini, Alessio

doi:10.1186/s40537-020-00288-8

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…In this paper, we have used the Isolation Forest method for outlier detection. The Isolation Forest method is a machine learning algorithm based on Random Forest and a Decision Trees classifier used to classify anomalies in large data sets [21]. Isolation Forest is an unsupervised machine learning algorithm for anomaly detection.…”

Section: Outlier Rejectionmentioning

confidence: 99%

A Novel Ensemble Framework for an Intelligent Intrusion Detection System

Seth¹,

Chahal

Singh

2021

IEEE Access

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Background: Building an effective Intrusion detection system in a multi-attack classification environment is challenging due to the diversity of modern, sophisticated attacks. High-performance classification methods are needed for Intrusion Detection Systems as attackers can establish intrusive methods and easily evade the detection tools deployed in a computing environment. Moreover, it is challenging to use a single classifier to efficiently detect all kinds of attacks. Aims: To propose a unique ensemble framework that can effectively detect different attack categories. Method: The proposed approach is based on building an ensemble by ranking the detection ability of different base classifiers to identify various types of attacks. The F1-score of an algorithm is used to compute the rank matrix for different attack categories. For final prediction algorithm's output for an attack is only considered if the algorithm has the highest F1-Score in the rank matrix for the particular attack category. This approach contrasts with the voting approach where the final classification is based on the voting of all classifiers in the ensemble irrespective of the fact if the algorithm is efficient enough to detect that attack or not. Results: With the proposed method, the final accuracy obtained is 96.97 %, a recall rate of 97.4%, and a better attack detection rate than the baseline classifiers and other existing approaches for different attack categories.

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Section: Outlier Rejectionmentioning

confidence: 99%

A Novel Ensemble Framework for an Intelligent Intrusion Detection System

Seth¹,

Chahal

Singh

2021

IEEE Access

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“…Isolation Forest is an unsupervised anomaly detection method, which is suitable for continuous data. Isolation Forest uses a binary search tree structure (also known as "isolated tree") to isolate samples and detect outliers through the isolation of sample points, which is different from the anomaly detection algorithm that expresses the degree of alienation between samples by quantitative indexes such as distance and density [27]. Because most of the attention values in the TGAM are in the correct range, the number of outliers is small and there is a significant difference from most samples.…”

Section: The Design Of Data Transmission Via the Tgammentioning

confidence: 99%

Attention Optimization Method for EEG via the TGAM

Xie

2020

Computational and Mathematical Methods in Medicine

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Since the 21st century, noninvasive brain-computer interface (BCI) has developed rapidly, and brain-computer devices have gradually moved from the laboratory to the mass market. Among them, the TGAM (ThinkGear Asic Module) and its encapsulate algorithm have been adopted by many research teams and faculty members around the world. However, due to the limited development cost, the effectiveness of the algorithm to calculate data is not satisfactory. This paper proposes an attention optimization algorithm based on the TGAM for EEG data feedback. Considering that the data output of the TGAM encapsulate algorithm fluctuates greatly, the delay is high and the accuracy is low. The experimental results demonstrated that our algorithm can optimize EEG data, so that with the same or even lower delay and without changing the encapsulate algorithm of the module itself, it can significantly improve the performance of attention data, greatly improve the stability and accuracy of data, and achieve better results in practical applications.

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“…Currently, the main approaches to addressing the issue of abnormal values are anomaly detection and removal. Existing methods for detecting and removing outliers include projection-based techniques: employing technologies like LSH 25 and spatial filling curves 26 to transform the original data into a new space structure with reduced complexity, where outlier scores are defined based on the characteristics of the new space; Isolation Forest method, 27,28 which measures data anomalies through randomized partitioning decision trees; and the Local Outlier Factor (LOF) method: 29 evaluating outliers by comparing the density of data points with their neighbouring points. However, these existing outlier removal methods often assume that data follows a specific distribution, which may not hold true in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%