Partial Rule Match for Filtering Rules in Associative Classification

Refai,

doi:10.3844/jcssp.2014.570.577

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Thus, precipitation values falls into five levels shown in Table 4. Since people concern forecasting rainfalls more than forecasting sunny days [33,34], we defined Hitrate as:…”

Section: Methodsmentioning

confidence: 99%

Case-Based FCTF Reasoning System

Zhang

Li³

et al. 2015

Applied Sciences

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Case-based reasoning uses old information to infer the answer of new problems. In case-based reasoning, a reasoner firstly records the previous cases, then searches the previous case list that is similar to the current one and uses that to solve the new case. Case-based reasoning means adapting old solving solutions to new situations. This paper proposes a reasoning system based on the case-based reasoning method. To begin, we show the theoretical structure and algorithm of from coarse to fine (FCTF) reasoning system, and then demonstrate that it is possible to successfully learn and reason new information. Finally, we use our system to predict practical weather conditions based on previous ones and experiments show that the prediction accuracy increases with further learning of the FCTF reasoning system.

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“…Thus, precipitation values falls into five levels shown in Table 4. Since people concern forecasting rainfalls more than forecasting sunny days [33,34], we defined Hitrate as:…”

Section: Methodsmentioning

confidence: 99%

Case-Based FCTF Reasoning System

Zhang

Li³

et al. 2015

Applied Sciences

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A Novel Contrast Pattern Selection Method for Class Imbalance Problems

Loyola‐González

Martínez-Trinidad

Carrasco-Ochoa

et al. 2017

Lecture Notes in Computer Science

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Text Classification Using Modified Multi Class Association Rule

et al. 2016

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This paper presents text classification using a modified Multi Class Association Rule Method. The method is based on Associative Classification which combines classification with association rule discovery. Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. To overcome this problem the modified Multi-Class Association Rule Method was enhanced in two stages. In stage one the frequent pattern are represented using a proposed vertical data format to reduce the text dimensionality problem and in stage two the generated rule was pruned using a proposed Partial Rule Match to reduce the number of generated rules. The proposed method was tested on a text classification problem and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules.

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Partial Rule Match for Filtering Rules in Associative Classification

Cited by 3 publications

References 18 publications

Case-Based FCTF Reasoning System

Case-Based FCTF Reasoning System

A Novel Contrast Pattern Selection Method for Class Imbalance Problems

Text Classification Using Modified Multi Class Association Rule

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