Improving lazy decision tree for imbalanced classification by using skew-insensitive criteria

Su, Chong; Cao, Jie

doi:10.1007/s10489-018-1314-z

Cited by 14 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The root-cause-tracing algorithm proposed in this paper selects old samples similar to a new sample when entering it into the database and combines the two phases to construct a system of equations to find the source of the fault. This method does not use all the previous data, but selects it selectively, so it has the characteristics of being "lazy" [25]. And this root-cause-tracing algorithm not only refers to past experience, but also fully combines the current situation, so can effectively deal with new sudden failures.…”

Section: Construction Of Root-cause-tracing Algorithmmentioning

confidence: 99%

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

Jing,

Li,

Song

et al. 2024

Algorithms

View full text Add to dashboard Cite

The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper focuses on the paramount looper equipment in the finishing rolling area, utilizing it as a case study to investigate approaches for identifying the origins of instabilities, specifically when faced with inadequate looper performance. Initially, the paper establishes the equipment process accuracy evaluation (EPAE) model for the looper, grounded in the precision of the looper’s operational process, to accurately depict the looper’s functioning state. Subsequently, it delves into the interplay between the EPAE metrics and overall production stability, advocating for the use of EPAE scores as direct indicators of production stability. The study further introduces a novel algorithm designed to trace the root causes of issues, categorizing them into material, equipment, and control factors, thereby facilitating on-site fault rectification. Finally, the practicality and effectiveness of this methodology are substantiated through its application on the 2250 hot rolling equipment production line. This paper provides a new approach for fault tracing in the hot rolling process.

show abstract

Section: Construction Of Root-cause-tracing Algorithmmentioning

confidence: 99%

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

Jing,

Li,

Song

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Hellinger Distance (HD) is related to Bhattacharyya Distance and it is part of the f-divergences family [78]. Studies presented in [84], [85] showed that Hellinger Distance can be used in classification. On the current scenario, this distance has been very used in machine learning, even as an alternative to methods such as entropy, aiming to detect failures in the classifiers [86] and breakpoints on the performance of those classifiers [87].…”

Section: ) Hellinger Distancementioning

confidence: 99%

“…On the current scenario, this distance has been very used in machine learning, even as an alternative to methods such as entropy, aiming to detect failures in the classifiers [86] and breakpoints on the performance of those classifiers [87]. Furthermore, according to the literature, Hellinger Distance has been used in many parametric models being very successful on solving problems of statistical estimation [84], [85]. The calculation function is obtained from two probability distributions p and q as follows [85]:…”

Section: ) Hellinger Distancementioning

confidence: 99%

See 1 more Smart Citation

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

View full text Add to dashboard Cite

Internet traffic classification aims to identify the kind of Internet traffic. With the rise of traffic encryption and multi-layer data encapsulation, some classic classification methods have lost their strength. In an attempt to increase classification performance, Machine Learning (ML) strategies have gained the scientific community interest and have shown themselves promising in the future of traffic classification, mainly in the recognition of encrypted traffic. However, some of these methods have a high computational resource consumption, which make them unfeasible for classification of large traffic flows or in real-time. Methods using statistical analysis have been used to classify real-time traffic or large traffic flows, where the main objective is to find statistical differences among flows or find a pattern in traffic characteristics through statistical properties that allow traffic classification. The purpose of this work is to address statistical methods to classify Internet traffic that were little or unexplored in the literature. This work is not generally focused on discussing statistical methodology. It focuses on discussing statistical tools applied to Internet traffic classification Thus, we provide an overview on statistical distances and divergences previously used or with potential to be used in the classification of Internet traffic. Then, we review previous works about Internet traffic classification using statistical methods, namely Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback-Leibler (KL) divergences, Support Vector Machines (SVM), Correlation Information (Pearson Correlation), Kolmogorov-Smirnov and Chi-Square tests, and Entropy. We also discuss some open issues and future research directions on Internet traffic classification using statistical methods.

show abstract

“…To improve the performance of standard DTs, several splitting criteria are proposed to construct DTs in Distinct Class based Splitting Measure (DCSM) [Chandra et al, 2010], Hellinger Distance Decision Tree (HDDT) [Cieslak and Chawla, 2008] and Class Confidence Proportion Decision Tree (CCPDT) [Liu et al, 2010]. Besides these, to deal with class imbalance problem in Lazy DT construction, two skew insensitive split criteria based on Hellinger distance and K-L divergence are proposed in [Su and Cao, 2019]. Since Lazy DTs use the test instance to make splitting decisions, in this paper, we omit it from our discussion.…”

Section: Introductionmentioning

confidence: 99%

Inter-node Hellinger Distance based Decision Tree

Akash

Kadir

Ali

et al. 2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. We prove that this ensures the mutual exclusiveness between the child nodes. The weight term in iHDw is concerned with the purity of individual child node considering the class imbalance problem. The combination of the distance and weight term in iHDw thus favors a partition where child nodes are purer and mutually exclusive, and skew insensitive. We perform an experiment over twenty balanced and twenty imbalanced datasets. The results show that decision trees based on iHD win against six other state-of-the-art methods on at least 14 balanced and 10 imbalanced datasets. We also observe that adding the weight to iHD improves the performance of decision trees on imbalanced datasets. Moreover, according to the result of the Friedman test, this improvement is statistically significant compared to other methods.

show abstract

Improving lazy decision tree for imbalanced classification by using skew-insensitive criteria

Cited by 14 publications

References 31 publications

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

Inter-node Hellinger Distance based Decision Tree

Contact Info

Product

Resources

About