Rule Extraction from Support Vector Machines

Diederich, Joachim

doi:10.1007/978-3-540-75390-2

Cited by 37 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also requires all the features to be numerical, as the BT data set's predominant feature type is categorical this will require transforming the data set. Approaches exist to extract human readable rules from SVMs [15], [2].…”

Section: B Rule Induction Of Pre-eventsmentioning

confidence: 99%

A Rule Induction Approach to Forecasting Critical Alarms in a Telecommunication Network

Wrench

Stahl

Fatta

et al. 2019

2019 International Conference on Data Mining Workshops (ICDMW)

View full text Add to dashboard Cite

This paper proposes a white box method of predicting critical alarms so they can be mitigated and understood by engineers. Forecasting these alarms will avoid outages and maintain the agreed service level which is beneficial to both the provider of telecommunication services and the consumers. The paper evaluates several item set mining approaches on a set of alarms of the British Telecom (BT) national telecommunication network and proposes a novel transformation of the data to enable the discovery of patterns undetectable by current item set mining approaches. The result is a method for rule induction that predicts alarms with high precision using a wide range of features.

show abstract

Section: B Rule Induction Of Pre-eventsmentioning

confidence: 99%

A Rule Induction Approach to Forecasting Critical Alarms in a Telecommunication Network

Wrench

Stahl

Fatta

et al. 2019

2019 International Conference on Data Mining Workshops (ICDMW)

View full text Add to dashboard Cite

show abstract

Comparative study of support vector machines and random forests machine learning algorithms on credit operation

et al. 2020

View full text Add to dashboard Cite

Summary Corporate insolvency has significant adverse effects on an economy. With the number of multinationals increasing rapidly, corporate bankruptcy can severely disrupt the global financial environment. However, multinationals do not fail instantaneously; objective strategies combined with a rigorous analysis of both qualitative and quantifiable data can go a long way in identifying an organization's financial risks. Recent advancements in information and communication technologies have made data collection and storage an easy task. The challenge becomes mining the appropriate data about a company's financial risks and implementing it in forecasting a company's insolvency probabilities. In recent years, machine learning has been incorporated into big data analytics owing to its massive success in learning complex models. Machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks, Gaussian Processes, and Adaptive Learning have been used in the analysis of Big Data to predict the financial risks of companies. In this paper, credit scoring is explored with regards to data processed using the collateral as an independent variable. The obtained results indicate that RF algorithm is promising for use in credit risk management. This research shows the advantages of the RF approach over the SVM algorithm are its speed and operational simplicity, and SVM has the benefit of higher classification accuracy than RF. The paper compares the SVM and RF algorithms to forecast the recovered value in a credit task. The execution of the projected intelligent systems uses tests and algorithms for authentication of the projected model.

show abstract

Variants of Support Vector Machines

Abe

2010

Support Vector Machines for Pattern Classification

View full text Add to dashboard Cite

Rule Extraction from Support Vector Machines

Cited by 37 publications

References 17 publications

A Rule Induction Approach to Forecasting Critical Alarms in a Telecommunication Network

A Rule Induction Approach to Forecasting Critical Alarms in a Telecommunication Network

Comparative study of support vector machines and random forests machine learning algorithms on credit operation

Variants of Support Vector Machines

Contact Info

Product

Resources

About