Application of the C4.5 classifier to building an expert system for ion chromatography

Mulholland, M.; Hibbert, David Brynn; Haddad, P.R.; Sammut, Claude

doi:10.1016/0169-7439(95)80010-7

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The advantages of the C4.5 algorithm are that it can handle continuous and discrete attributes, then it can handle training data with missing values, can process large and complex datasets, and use gain ratios to improve information gain [11]. The disadvantages of the C4.5 Algorithm are:bias towards small distribution [12]. The C4.5 algorithm works by separating samples based on the attributes that produce the highest information gain value.…”

Section: C45 Decision Tree Algorithmmentioning

confidence: 99%

Implementation of the c4.5 decision tree learning algorithm for sentiment analysis in e-commerce application reviews on google play store

Ikhsanti

Fauziah

Perwira

2021

Comput. Inf. Process. Lett.

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Objective: Forknowing the level of accuracy of the C4.5 Decision Tree Learning Algorithm in sentiment analysis of reviews of e-commerce applications on the google play store.Design/method/approach: Using C4.5 AlgorithmDecision Tree Learning.Results: This study uses a confusion matrix test with a comparison of 80% for training data and 20% for test data, where 750 is used for training data and 190 is used for test data. This test obtained an average accuracy of 92.63%, precision 69.58%, and recall 69.99%.Authenticity/state of the art: In this study using the C4.5 Algorithm Decision Tree Learningto conduct sentiment analysis of e-commerce reviews, which use the gain value to perform feature selection. There are four categories, namely display, service, access, and product. The data in this study were obtained from the google play store.

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Section: C45 Decision Tree Algorithmmentioning

confidence: 99%

Implementation of the c4.5 decision tree learning algorithm for sentiment analysis in e-commerce application reviews on google play store

Ikhsanti

Fauziah

Perwira

2021

Comput. Inf. Process. Lett.

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“…This was the first of a number of systems based on SCRDR, such as the XRDR general purpose classification system. Noteworthy SCRDR extensions are Time Course RDR (TCRDR) which was used by PEIRS and Recursive RDR (RRDR) (Mulholland et al ., 1993). TCRDR used various functions and features to handle time course data.…”

Section: Variations Extensions and Implementationsmentioning

confidence: 99%

“…A heuristic was then used to choose which conclusions to accept and these were used as inputs and inferencing commenced from the beginning again. Another implementation combined RRDR with Possible RDR (PRDR) (Mulholland et al ., 1993) and reported all last true conditions after taking any branches that might possibly be true. PRDR considers a subset of the branches used in RRDR.…”

Section: Variations Extensions and Implementationsmentioning

confidence: 99%

“…Also standard MCRDR inferencing has been enhanced to also allow recursive evaluation and backtracking as in the case of intermediate conclusion rule nodes, which allow the engine to jump to a rule in another part of the KB and backtrack to the root node. This is similar to the inferencing mechanism used with multiple SCRDR KB for configuring an ion chromatographer (Mulholland et al ., 1993).

Figure 6A partial CRDR for our animal domain…”

Section: Variations Extensions and Implementationsmentioning

confidence: 99%

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Two decades of Ripple Down Rules research

Richards

2009

The Knowledge Engineering Review

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Ripple Down Rules (RDR) were developed in answer to the problem of maintaining medium to large rule-based knowledge systems. Traditional approaches to knowledge-based systems gave little thought to maintenance as it was expected that extensive upfront domain analysis involving a highly trained specialist, the knowledge engineer, and the time-poor domain expert would produce a complete model capturing what was in the expert’s head. The ever-changing, contextual and embrained nature of knowledge were not a part of the philosophy upon which they were based. RDR was a paradigm shift, which made knowledge acquisition and maintenance one and the same thing by incrementally acquiring knowledge as domain experts directly interacted with naturally occurring cases in their domain. Cases played an integral part of the acquisition process by motivating the capture of new knowledge, framing the context in which new knowledge would apply and ensuring that previously correctly classified cases remained so by requiring that the classification of the new case distinguish it from the system’s classification and be justified by features of the new case. RDR has moved beyond its first representation which handled single classification tasks within the domain of pathology to support multiple conclusions across a wide range of domains such as help-desk support, email classification and RoboCup and problem types including configuration, simulation, planning and natural language processing. This paper reviews the history of RDR research over the past two decades with a view to its future.

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Learning classification rules from an ion chromatography database using a genetic based classifier system

Kampen

Ramadan

Mulholland

et al. 1997

Analytica Chimica Acta

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Application of the C4.5 classifier to building an expert system for ion chromatography

Cited by 7 publications

References 11 publications

Implementation of the c4.5 decision tree learning algorithm for sentiment analysis in e-commerce application reviews on google play store

Implementation of the c4.5 decision tree learning algorithm for sentiment analysis in e-commerce application reviews on google play store

Two decades of Ripple Down Rules research

Learning classification rules from an ion chromatography database using a genetic based classifier system

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