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ACN: An Associative Classifier with Negative Rules

Gourab Kundu, Md. Monirul Islam, Sirajum Munir, Md. Faizul Bari
2008 2008 11th IEEE International Conference on Computational Science and Engineering  
Over the years, a number of associative classifiers based on positive rules have been proposed in literature.  ...  The target of this paper is to improve classification accuracy by using both negative and positive class association rules without sacrificing performance.  ...  association rules and then uses both positive and negative rules to build a classifier.  ... 
doi:10.1109/cse.2008.48 dblp:conf/cse/KunduIMB08 fatcat:nnnofm52ije3hjp3unci35rx2a

ACN: An associative classifier with negative rules

Gourab Kundu, Md. Monirul Islam, Sirajum Munir
2008 2008 IEEE International Conference on System of Systems Engineering  
Over the years, a number of associative classifiers based on positive rules have been proposed in literature.  ...  The target of this paper is to improve classification accuracy by using both negative and positive class association rules without sacrificing performance.  ...  association rules and then uses both positive and negative rules to build a classifier.  ... 
doi:10.1109/sysose.2008.4724163 dblp:conf/sysose/KunduIM08 fatcat:4magb7wzi5abhdias7d2c4mqra

Algorithm for classification based on positive and negative class association rules

Luo Junwei, Luo Huimin
2010 2010 3rd International Conference on Computer Science and Information Technology  
In order to solve the problem of "difficult to build precise classifier", the paper presents a new algorithm for classification which integrates positive class association rules and negative class association  ...  The algorithm applies Apriori method and correlation between itemsets and class labels to compute all positive and negative class association rules from training dataset.  ...  A new algorithm is presented to generate all positive and negative class association rules and to build an accurate classifier.  ... 
doi:10.1109/iccsit.2010.5564641 fatcat:dzjk6c3b3rafbn5z74tm2gyr6y

An associative classifier based on positive and negative rules

Maria-Luiza Antonie, Osmar R. Zaïane
2004 Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery - DMKD '04  
We propose a new framework that uses different types of association rules, positive and negative.  ...  In this paper we propose a new algorithm to discover at the same time positive and negative association rules.  ...  Acknowledgements: This work was partially supported by Alberta Ingenuity Fund, iCORE and NSERC Canada.  ... 
doi:10.1145/1008694.1008705 dblp:conf/dmkd/AntonieZ04 fatcat:uu3qtkcuvzby3ohityev4ntgmi

Classification Inductive Rule Learning with Negated Features [chapter]

Stephanie Chua, Frans Coenen, Grant Malcolm
2010 Lecture Notes in Computer Science  
Comparisons are also made with Associative Rule Learning (ARL) in the context of multi-class text classification.  ...  The emphasis is on generating the negation of features while rules are being "learnt"; rather than including (or deriving) the negation of all features as part of the input.  ...  Examples of rule-based classifiers include Associative Rule Learning (ARL) systems such as Classification based on Multiple class-Association Rules (CMAR) [12] , Classification based on Predictive Association  ... 
doi:10.1007/978-3-642-17316-5_12 fatcat:qgw5kvqr2nexzdxynpzasbcs6i

A Novel Text Classification Approach Based on Enhanced Association Rule [chapter]

Jiangtao Qiu, Changjie Tang, Tao Zeng, Shaojie Qiao, Jie Zuo, Peng Chen, Jun Zhu
2007 Lecture Notes in Computer Science  
The current research on association rule based text classification neglected several key problems.  ...  First, weights of elements in profile vectors may have much impact on generating classification rules. Second, traditional association rule lacks semantics.  ...  Thus we need to further generate PARs based on these classification rules.  ... 
doi:10.1007/978-3-540-73871-8_24 fatcat:7qeb7lyuuzcldn32spvmxpenhu

Hybrid Approach for Prediction of Cardiovascular Disease Using Class Association Rules and MLP

Srinivas Konda, Kavitha Rani Balmuri, Ramasubba Reddy Basireddy, Ravindar Mogili
2016 International Journal of Electrical and Computer Engineering (IJECE)  
classifier and artificial neural networks (MLP).  ...  The model thus generated may be deployed to classify new examples or enable a better comprehension of available data.  ...  We are proposing an approach to perform Classification based on Positive and Negative Association Rules which are known as Class Association Rules.  ... 
doi:10.11591/ijece.v6i4.9902 fatcat:wysskdrbm5ey5clqtz2sd3ytku

Applying sequential rules to protein localization prediction

Elena Baralis, Silvia Chiusano, Riccardo Dutto
2008 Computers and Mathematics with Applications  
In this paper we present a new classifier based on sequential classification rules for protein localization prediction.  ...  To further improve classification performance, an SVM classifier is used to process data not covered by means of the sequential rule classifier.  ...  These forms are based on the abstractions of general and specialistic rule, and compact rule.  ... 
doi:10.1016/j.camwa.2006.12.086 fatcat:psl7zlhqsrg2dasktpecxdzu2y

Sentiment Classification of Hotel Reviews in Social Media with Decision Tree Learning

Stanimira Yordanova, Dorina Kabakchieva
2017 International Journal of Computer Applications  
or negative.  ...  The results from the classifier evaluation are compared and discussed. The three classification models are also applied on new unseen data for predicting opinion of hotel guests.  ...  Based on rules 3, 6, 8, 9, the important words for positive classification are good, friendli, great and excel.  ... 
doi:10.5120/ijca2017912806 fatcat:hrajiybdq5ae5f2jcth3a4lsdu

Techniques for efficient empirical induction [chapter]

Geoffrey I. Webb
1990 Lecture Notes in Computer Science  
The LEI algorithm will always find the simplest nondisjunctive rule that correctly classifies all examples of a single class where such a rule exists.  ...  It derives a classification procedure in the form of a set of predicate logic classification rules.  ...  Acknowledgements Part of this research was conducted in the Griffith University School of Computing and Information Technology.  ... 
doi:10.1007/3-540-52062-7_82 fatcat:d6gk32tlv5hnph6ydbajaf4lo4

Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns [chapter]

Yanchang Zhao, Huaifeng Zhang, Shanshan Wu, Jian Pei, Longbing Cao, Chengqi Zhang, Hans Bohlscheid
2009 Lecture Notes in Computer Science  
The central technique is building sequence classification using both positive and negative sequential patterns.  ...  The existing sequence classification methods based on sequential patterns consider only positive patterns.  ...  Peter Newbigin and Mr. Brett Clark from Business Integrity Review Operations Branch, Centrelink, Australia for their support of domain knowledge and helpful suggestions.  ... 
doi:10.1007/978-3-642-04174-7_42 fatcat:xqmm4citfjfifo7mra6hambtwy

A Joint Model for Chinese Microblog Sentiment Analysis

Yuhui Cao, Zhao Chen, Ruifeng Xu, Tao Chen, Lin Gui
2015 Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing  
The classification results outputted by these two classifiers are merged as the final classification results.  ...  Firstly, a SVM Classifier is constructed using N-gram, N-POS and sentiment lexicons features.  ...  positive neutral neutral negative neutral neutral neutral positive neutral neutral negative neutral positive negative negative negative positive positive Table 3 : Merging rules for  ... 
doi:10.18653/v1/w15-3111 dblp:conf/acl-sighan/CaoCXCG15 fatcat:tr3kuj6msjet5jt5ctqvlgi3qy

A Comprehensive Analysis on Associative Classification in Medical Datasets

D. Sasirekha, A. Punitha
2015 Indian Journal of Science and Technology  
Association rule mining along with classification technique is capable of finding informative patterns from large data sets.  ...  This paper presents a detailed study on associative classification and the phases of associative classification procedure.  ...  Then positive and negative association rules are used to build a classifier.  ... 
doi:10.17485/ijst/2015/v8i33/80081 fatcat:wjggaav4kbbotedmbvhvlvraeu

A C4.5 algorithm for english emotional classification

Phu Vo Ngoc, Chau Vo Thi Ngoc, Tran Vo Thi Ngoc, Dat Nguyen Duy
2017 Evolving Systems  
negative polarity are created by the decision tree. Classifying sentiments of one English document is identified based on the association rules of the positive polarity and the negative polarity.  ...  Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews.  ...  Li and Liu 2014; Turney 2002; Lee et al. 2002; Zyl 2002; Le Hegarat-Mascle et al. 2002; Ferro-Famil and Pottier 2002; Chaovalit and Zhou 2005; Lee and Lewicki 2002; Gllavata et al. 2004) , to understand  ... 
doi:10.1007/s12530-017-9180-1 fatcat:ks3xxs5v4neypl7aeibnnzzrru

CHISC-AC: Compact Highest Subset Confidence-Based Associative Classification^|^sup1;

S P Syed Ibrahim, K R Chandran, C J Kabila Kanthasamy
2014 Data Science Journal  
This paper proposes a Compact Highest Subset Confidence-Based Associative Classification scheme that generates compact subsets based on information gain and classifies the new samples without constructing  ...  The associative classification method integrates association rule mining and classification.  ...  False Negative (FN) corresponds to the number of positive examples wrongly predicted as negative class by the classifier. 4.  ... 
doi:10.2481/dsj.14-035 fatcat:iag37tsdfzb2fe5lhcgu3nt5my
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