Increasing the effectiveness of associative classification in terms of class imbalance by using a novel pruning algorithm

Chen, Wen-Chin; Hsu, Cheng–Che; Chu, Yu-Chun

doi:10.1016/j.eswa.2012.05.009

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…PCAR performs changes in the pruning method, scoring procedure and rule sorting index of CBA to achieve such purpose. Another associative classification algorithm for imbalanced learning by improving the scoring based on association (SBA) approach is proposed by Chen et al 66 This improvement is done by combining the scoring with pruning of association rules in probabilistic classification based on associations (PCBA). Confidence is increased using undersampling and deciding different minimum support and confidence for rules of each class on the basis of distribution to adjust CBA for the forming of PCBA that also removes the pruning rules for the least error rate.…”

Section: Cost-sensitive and Ensemble Approachesmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

108

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Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

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Section: Cost-sensitive and Ensemble Approachesmentioning

confidence: 99%

A review on classification of imbalanced data for wireless sensor networks

Patel

Rajput

Reddy

et al. 2020

International Journal of Distributed Sensor Networks

108

View full text Add to dashboard Cite

show abstract

“…The pruning method tries to choose a minimum number of rule sets, with classifying training instance correctly, to achieve the minimum error rate. The default class is selected as the majority class in the remaining instance that is not satisfied by any rule in the final classifier [30].…”

Section: The Cba-cb Algorithmmentioning

confidence: 99%

Knowledge discovery and sequence-based prediction of pandemic influenza using an integrated classification and association rule mining (CBA) algorithm

Kargarfard

Sami

Ebrahimie

2015

Journal of Biomedical Informatics

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Pandemic influenza is a major concern worldwide. Availability of advanced technologies and the nucleotide sequences of a large number of pandemic and non-pandemic influenza viruses in 2009 provide a great opportunity to investigate the underlying rules of pandemic induction through data mining tools. Here, for the first time, an integrated classification and association rule mining algorithm (CBA) was used to discover the rules underpinning alteration of non-pandemic sequences to pandemic ones. We hypothesized that the extracted rules can lead to the development of an efficient expert system for prediction of influenza pandemics. To this end, we used a large dataset containing 5373 HA (hemagglutinin) segments of the 2009 H1N1 pandemic and non-pandemic influenza sequences. The analysis was carried out for both nucleotide and protein sequences. We found a number of new rules which potentially present the undiscovered antigenic sites at influenza structure. At the nucleotide level, alteration of thymine (T) at position 260 was the key discriminating feature in distinguishing non-pandemic from pandemic sequences. At the protein level, rules including I233K, M334L were the differentiating features. CBA efficiently classifies pandemic and non-pandemic sequences with high accuracy at both the nucleotide and protein level. Finding hotspots in influenza sequences is a significant finding as they represent the regions with low antibody reactivity. We argue that the virus breaks host immunity response by mutation at these spots. Based on the discovered rules, we developed the software, "Prediction of Pandemic Influenza" for discrimination of pandemic from non-pandemic sequences. This study opens a new vista in discovery of association rules between mutation points during evolution of pandemic influenza.

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“…the so-called class association rules (CARs). Thus, only rules of the form A ⇒ c i , where c i denotes a possible class, are generated [42]. …”

Section: Methodsmentioning

confidence: 99%

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments

Kargarfard

Sami

Mohammadi‐Dehcheshmeh

et al. 2016

BMC Genomics

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BackgroundRecent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range.MethodsTo obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment.ResultWe found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions.ConclusionHost range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3250-9) contains supplementary material, which is available to authorized users.

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Increasing the effectiveness of associative classification in terms of class imbalance by using a novel pruning algorithm

Cited by 13 publications

References 19 publications

A review on classification of imbalanced data for wireless sensor networks

A review on classification of imbalanced data for wireless sensor networks

Knowledge discovery and sequence-based prediction of pandemic influenza using an integrated classification and association rule mining (CBA) algorithm

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments

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