Induction of ripple-down rules applied to modeling large databases

Gaines, Brian R.; Compton, Paul

doi:10.1007/bf00962234

Cited by 197 publications

(84 citation statements)

References 5 publications

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“…We further investigate the influence of α and β parameters of the RS metric function. The performance of RS metric is studied through various experimentations using the Weka [10] simulator on the following rule-based prediction models: DecisionTable [11], JRip [12], Nearest Neighbor with generalization (NNge) [13], PART [14], ConjunctiveRule [15] and Ridor [16] on the breakout dataset [1]. The breakout dataset consists of 236 samples of data from different users gathered Relevance As a Metric for Evaluating Machine Learning Algorithms 9 from the breakout area.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

Relevance as a Metric for Evaluating Machine Learning Algorithms

Gopalakrishna¹,

Özçelebi²,

Liotta

et al. 2013

Machine Learning and Data Mining in Pattern Recognition

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In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performance metric called Relevance Score for evaluating supervised learning algorithms. We evaluate the proposed metric through empirical analysis on a dataset gathered from an intelligent lighting pilot installation. In comparison to the commonly used Classification Accuracy metric, the Relevance Score proves to be more appropriate for a certain class of applications.

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Section: Experimentation and Resultsmentioning

confidence: 99%

Relevance as a Metric for Evaluating Machine Learning Algorithms

Gopalakrishna¹,

Özçelebi²,

Liotta

et al. 2013

Machine Learning and Data Mining in Pattern Recognition

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show abstract

“…Thus it performs a tree-like expansion of exceptions and the leaf has only default rules but no exceptions. The exceptions are a set of rules that predict the improper instances in default rules [Gaines & Compton, 1995].…”

Section: Ridor Methodsmentioning

confidence: 99%

Benchmarking the Data Mining Algorithms with Adaptive Neuro-Fuzzy Inference System in GSM Churn Management

Karahoca¹,

Karahoca²,

Aydın³

2009

Data Mining and Knowledge Discovery in Real Life Applications

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“…In WEKA, a rule which identifies the maximum number of correct instances is selected as its single rule. To do so, the most recurrent class of that attribute value is determined [41]. If 2 rules possess identical error rate then it selects one of the rules at random [42].…”

Section: Onermentioning

confidence: 99%

Usage of Machine Learning for Intrusion Detection in a Network

Ahlawat¹

2016

ijcna

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-Increase in volume and intensity of network attacks, forcing the business systems to revamp their network security solutions in order to avoid huge financial losses. Intrusion Detection Systems are one of the most essential security solutions in order to ensure the security of any network. Considering huge volumes of network data and complex nature of intrusions, the performance optimization of Network Intrusion Detection System became an open problem that is gaining more and more attention from the researchers nowadays. The objective of this paper is to identify a machine learning algorithm that provides high accuracy and real-time system application. This paper evaluates the performance of 15 different machine learning algorithms using NSL-KDD dataset on the basis of false discovery rate, average accuracy, root mean squared error and model building time. Firstly, 5 machine learning algorithms out of 15 are chosen on the basis of maximum accuracy and minimum error in WEKA. Simulation of these machine learning algorithms is performed using 10-fold cross validation. Thereafter, the best machine learning algorithm is selected on the basis of maximum accuracy and minimum model building time so that it can be readily implemented in real-time Intrusion Detection Systems.

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Induction of ripple-down rules applied to modeling large databases

Cited by 197 publications

References 5 publications

Relevance as a Metric for Evaluating Machine Learning Algorithms

Relevance as a Metric for Evaluating Machine Learning Algorithms

Benchmarking the Data Mining Algorithms with Adaptive Neuro-Fuzzy Inference System in GSM Churn Management

Usage of Machine Learning for Intrusion Detection in a Network

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