Studying Netconf in Hybrid Rule Ordering Strategies for Associative Classification

Hernández-León, Raudel; Hernández-Palancar, José; Carrasco-Ochoa, Jesús Ariel; Martínez-Trinidad, José Fco.

doi:10.1007/978-3-319-07491-7_6

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…To overcome these shortcomings, Hernández-León et al [23] designed a new measure, named NETCONF, to generate and rank the set of CARs in 2012. Afterwards, by integrating the NETCONF strategy with the common Support-Confidence, Hernández-León et al [24] improved their initial CAR-NF approach to a hybrid rule ordering strategy. Experiments demonstrate that the novel hybrid strategy is superior to the traditional CSC approach in terms of classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

Cao

Zhong

et al. 2020

IEEE Access

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Associative Classification (AC) is envisioned as one of the most attractive classification approaches to facilitate managers for prediction and decision making in a highly accurate and easily interpretable way. The most of existing AC algorithms mainly focus on the two static metrics of association rules: support and confidence. However, in this paper, we identify a potential limitation of the confidence and further point out these AC algorithms merely consider the explicit expression of classification information and knowledge of Class Association Rules (CARs) and neglect the implicit expression of classification information and knowledge of CARs. And even the explicit expression of classification information and knowledge of CARs is equivalent, the implicit expression of classification information and knowledge of them may be different. Thus, these CARs will cause different influence on prediction or decision-making in terms of the interpretability and rationality of classification, and will result in predictive bias or decision-making bias in practice. In response to this drawback, we introduce the notion of information entropy and propose an innovative approach for associative classification based on information entropy of frequent attribute set, named EAC. Different from the existing schemes, the proposed EAC algorithm enjoys the following promising merits: (1) lower predictive bias or decision-making bias by levering the information entropy of frequent attribute set; (2) better interpretability and rationality by setting higher support threshold; (3)setting global optimum parameter dynamically through repeated trials with reducing the dimension of data sets. Experiments on 20 well-known benchmark data sets demonstrate that our EAC approach is highly competitive to other state-of-the-art AC algorithms in terms of predictive bias or decision-making bias, interpretability, and efficiency, which can be used to construct a new classifier efficiently and effectively in many realistic scenarios. INDEX TERMS Data mining, associative classification, information entropy, frequent attribute set.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

Cao

Zhong

et al. 2020

IEEE Access

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“…Nota-se, diante dos trabalhos expostos, que o assunto é relevante e que em nenhum deles as medidas são avaliadas em conjunto, de maneira agregada, o que este trabalho se propõe a fazer. Outros trabalhos relacionados ao tema podem ser vistos em [Abdelhamid et al 2012] e [Hernández-León et al 2014].…”

Section: Trabalhos Relacionadosunclassified

Aggregating Interestingness Measures in Associative Classifiers

Silva

Carvalho

2018

Anais Do XV Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2018)

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A classificação associativa, a qual vem sendo muito utilizada em diversos domínios, visa a obtenção de um modelo preditivo em que o processo é baseado na extração de regras de associação. A geração do modelo ocorre em etapas, sendo uma delas voltadas a ordenar e podar um conjunto de regras. No que se refere a ordenação, uma das soluções é ranquear as regras por meio de medidas objetivas (MOs). O critério de ordenação impacta a acurácia do classificador. Nos trabalhos da literatura as MOs são exploradas individualmente. Diante do exposto, este trabalho tem por objetivo explorar a agregação de medidas, em que várias MOs são consideradas ao mesmo tempo, no contexto de classificadores associativos.

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“…Luyu Gao [6] considered the largest matrix as the matching pattern. Hernndez-Len [9] introduced the use of Netconf measure instead of support and confidence, then proposed an approach to rule ranking by combining Netconf with rules of a long length. Kiburm Song [10] applied cross-validation and aggregating rules to select ARs, defined a new concept of predict rate to measure algorithm prediction performance, removing redundant and low predictive power rules.…”

Section: Introductionmentioning

confidence: 99%

A Novel Selection and Matching of Patterns Model for Traffic Prediction in Wireless Network

Shen¹,

Gao²

2018

dtcse

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Data mining techniques are becoming increasing popular and important in the big data era. Among them, frequent patterns mining technique has matured, with this technique, we can obtain the association rules to predict the data in cellular network. Existing research mainly focus on the frequent item sets mining, rarely involves in the time series and FPM algorithm usually used user-defined minimum support in various training datasets, which is rigorous, may influence the number of the frequent patterns and fail to select the reasonable patterns. Moreover, traditional association rules mining only relies on single evaluation criterion such as confidence or support, which leads to select unsound rules. This paper introduces an optimized frequent pattern mining algorithm, improving the setting of support in the process of mining frequent sequences and presenting a new evaluation criterion for the candidate patterns. Experiments were conducted to select appropriate parameters, support and evaluation criterion. Furthermore, we apply the above conclusion to the cellular flow data prediction, and compare the runtime, matching rate, RMSE and MAPE of proposed algorithms with those of improved Markov algorithm to examine the effectiveness of algorithm.

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Studying Netconf in Hybrid Rule Ordering Strategies for Associative Classification

Cited by 3 publications

References 15 publications

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

A Novel Approach for Associative Classification Based on Information Entropy of Frequent Attribute Set

Aggregating Interestingness Measures in Associative Classifiers

A Novel Selection and Matching of Patterns Model for Traffic Prediction in Wireless Network

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