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Contrast Pattern Mining and Its Application for Building Robust Classifiers [chapter]

Kotagiri Ramamohanarao
2010 Lecture Notes in Computer Science  
It will also focus on some important real world applications that illustrate how contrast patterns can be applied effectively for building robust classifiers.  ...  Such ability can assist domain experts to understand their data and can help in building classification models. This presentation will introduce the techniques for contrasting data sets.  ...  GrC 2005: 435440 28) Hongjian Fan, Kotagiri Ramamohanarao: Fast Discovery and the Generalization of Strong Jumping Emerging Patterns for Building Compact and Accurate Classifiers. IEEE Trans.  ... 
doi:10.1007/978-3-642-16108-7_5 fatcat:gdfnoamfebcubgzj2qj7s5zzqm

Overview of Contrast Data Mining as a Field and Preview of an Upcoming Book

Guozhu Dong, James Bailey
2011 2011 IEEE 11th International Conference on Data Mining Workshops  
This report provides an overview of the field of contrast data mining and its applications, and offers a preview of an upcoming book on the topic.  ...  ; applications of contrast mining for fundamental data mining tasks such as classification and clustering; applications of contrast mining in bioinformatics, medicine, blog analysis, image analysis and  ...  Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of NSF.  ... 
doi:10.1109/icdmw.2011.133 dblp:conf/icdm/DongB11 fatcat:fmzo2uyxl5cjtgaazqoaikiggy

OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns [article]

Guozhu Dong, Sai Kiran Pentukar
2018 arXiv   pre-print
This paper presents a method called One-class Classification using Length statistics of Emerging Patterns Plus (OCLEP+).  ...  It does not use a mathematical model, and it does not use distance metrics. Experiments showed that OCLEP+ outperformed one-class SVM with linear, polynomial, and RBF kernels.  ...  Concluding Remarks This paper presented the OCLEP+ method for intrusion detection using oneclass training. The method is based on the use of minimal length of jumping emerging patterns.  ... 
arXiv:1811.09842v1 fatcat:yiwtw3wzunc55ezvmkldoj3z7a

A Study On Data Mining Techniques And Their Areas Of Application

2017 International Journal of Recent Trends in Engineering and Research  
that are being used in data mining projects such as association, classification, clustering, prediction, sequential pattern and decision trees.  ...  The process of extracting previously obscure, comprehensible and actionable information from expansive databases and utilizing it to make important business decisions .There are several data mining techniques  ...  One of the reasons for its popularity is because it is easy to build and simple to understand for users. Linear Regression is used to estimate a relationship between two variables.  ... 
doi:10.23883/ijrter.2017.3393.eo7o3 fatcat:g6ctcb5z25e6thihphm7iir6jm

Efficient Mining of Contrast Patterns and Their Applications to Classification

K. Ramamohanarao, J. Bailey, Hongjian Fan
2005 2005 3rd International Conference on Intelligent Sensing and Information Processing  
They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust classifiers. In this paper, we examine various kinds of contrast patterns.  ...  We also investigate efficient pattern mining techniques and discuss how to exploit patterns to construct effective classifiers.  ...  However, there are still interesting questions, such as finding more generalized patterns (e.g., contrast graph patterns and contrast sequence patterns) and defining richer language expressions for patterns  ... 
doi:10.1109/icisip.2005.1619410 fatcat:mkajvfymuzaqnfdgka3vqbbxwa

A Machine Learning based Robust Prediction Model for Real-life Mobile Phone Data [article]

Iqbal H. Sarker
2019 arXiv   pre-print
In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator  ...  In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model.  ...  Alan Colman, Swinburne University of Technology, Australia, and Dr. Ashad Kabir, Charles Sturt University, Australia for their relevant discussions.  ... 
arXiv:1902.07588v1 fatcat:oylakibpcnad7brktskheytcye

Appraisal of the Classification Technique in Data Mining of Student Performance using J48 Decision Tree, K-Nearest Neighbor and Multilayer Perceptron Algorithms

Faiza Umar, Najim Ussiph
2018 International Journal of Computer Applications  
speed, robustness and interpretability.  ...  pattern between the entry grades with which students enter university and their grades upon graduation.  ...  Hence the main objective of data mining is the application of methods and algorithms in order to discover and deduce new patterns from saved data. [3] Educational data mining is a new research area that  ... 
doi:10.5120/ijca2018916751 fatcat:stbxf3lf5rh7doow25hcpw4a3a

Review paper on adapting data stream mining concept drift using ensemble classifier approach

Nilima Motghare, Arvind Mewada
2014 IOSR Journal of Computer Engineering  
Data stream is massive, fast changing and infinite in nature. It is very natural that large amount of  ...  Finally, improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise.  ...  In contrast, ensemble classifier approaches build each model from a batch of training data using a traditional batch learning technique in such a way that this combination will improve the performance  ... 
doi:10.9790/0661-1654120123 fatcat:wyub5nwbgbfzpgwo5x3hbxwski

Python: A Quintessential approach towards Data Science

Aniket M. Wazarkar
2021 International Journal for Research in Applied Science and Engineering Technology  
Python has undoubtedly become paramount for data scientists mindful of cosmic and robust standard libraries which are used for analyzing and visualizing the data.  ...  In this paper, we will scrutinize various tools which are used by python programmers for efficient data analytics, their scope with contrast to other programming languages.  ...  It enables data analysts to build and implement predictive models.  ... 
doi:10.22214/ijraset.2021.35683 fatcat:pf67f6un6rghvaibjbolnztqmu

RobustSPAM for Inference from Noisy Longitudinal Data and Preservation of Privacy

Anna Palczewska, Jan Palczewski, Georgios Aivaliotis, Lukasz Kowalik
2017 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)  
In this work we introduce a robust temporal pattern mining framework for finding predictive patterns in complex timestamped multivariate and noisy data.  ...  The availability of complex temporal datasets in social, health and consumer contexts has driven the development of pattern mining techniques that enable the use of classical machine learning tools for  ...  Pattern Count In contrast to other temporal pattern mining algorithms the counting phase is challenging due to robust timestamps.  ... 
doi:10.1109/icmla.2017.0-137 dblp:conf/icmla/PalczewskaPAK17 fatcat:teuveqopsjgkza73t654l7edke

A Survey on CDPCF: Concise Discriminative Patterns Based Classification Framework

Ashwini Shahpurkar, Prof. S B Chaudhari
2018 IJARCCE  
Frequent pattern-based classification methods have shown to be very effective at classifying categorical or high dimensional sparse datasets  ...  handle both numerical and downright highlights and high dimensional highlights.  ...  It provides both the most robust and the most reliable predictions. It had potential to reduce the population size needed to measure a drug effect by 20%. It has more costly clinical trials.  ... 
doi:10.17148/ijarcce.2018.7104 fatcat:kzfjryb6cveilg7npqzq4q5t64

Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns

Shameek Ghosh, Jinyan Li, Longbing Cao, Kotagiri Ramamohanarao
2017 Journal of Biomedical Informatics  
as well as from the coupling of such patterns to build powerful risk stratification models for the septic shock patients.  ...  Methods: It is widely understood that physiological conditions of patients on variables such as blood pressure and heart rate are suggestive to gradual changes over a certain period of time prior to the  ...  In classification problems, discriminative patterns have strong associations with class sensitive datasets, making them suitable for use as potential variables or features, while building a robust classifier  ... 
doi:10.1016/j.jbi.2016.12.010 pmid:28011233 fatcat:xwon6rru2nbd3b6w2du43ojyqm

Classifying High-Dimensional Text and Web Data Using Very Short Patterns

Hassan H. Malik, John R. Kender
2008 2008 Eighth IEEE International Conference on Data Mining  
In this paper, we propose the "Democratic Classifier", a simple, democracy-inspired patternbased classification algorithm that uses very short patterns for classification, and does not rely on the minimum  ...  In addition, we respect "each voter's opinion" by simultaneously adding shared patterns to all applicable classes, and then apply a novel power law based weighing scheme, instead of making binary decisions  ...  Acknowledgements The authors would like to acknowledge that Harmony served as the initial inspiration for this research, and thank Jianyong Wang for providing us the executables.  ... 
doi:10.1109/icdm.2008.139 dblp:conf/icdm/MalikK08 fatcat:riql2nfacvckdcv3zvhw3oh2bu

A Review on Various Methods of Intrusion Detection System

2020 Computer Engineering and Intelligent Systems  
Data mining is used to clean, classify and examine large amount of network data. Since a large volume of network traffic that requires processing, we use data mining techniques.  ...  This paper presents the survey on data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, thereby helping the users  ...  First, they build a diverse pool of mining models to improve robustness of a variety of mining algorithms.  ... 
doi:10.7176/ceis/11-1-02 fatcat:ubcjgwamsrbzlovj5isaaeqpc4

ART: A Hybrid Classification Model

Fernando Berzal, Juan-Carlos Cubero, Daniel Sánchez, José María Serrano
2004 Machine Learning  
Our method is a generalized "Separate and Conquer" algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.  ...  ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees.  ...  ART has demonstrated that it can build classifiers which stand out because of their simplicity and robustness. ART classifiers are easy for human users to understand.  ... 
doi:10.1023/b:mach.0000008085.22487.a6 fatcat:td2p3sot3bfttez7dm7xbtsnkm
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