From global to local and viceversa: uses of associative rule learning for classification in imprecise environments

[1]  Wilhelmiina Hämäläinen,et al.  StatApriori: an efficient algorithm for searching statistically significant association rules , 2010, Knowledge and Information Systems.

[2]  Gianni Costa,et al.  Rule Learning with Probabilistic Smoothing , 2009, DaWaK.

[3]  Eugenio Cesario,et al.  Boosting text segmentation via progressive classification , 2008, Knowledge and Information Systems.

[4]  Nikolaj Tatti,et al.  Maximum entropy based significance of itemsets , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[5]  Jiawei Han,et al.  Discriminative Frequent Pattern Analysis for Effective Classification , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[6]  Fadi A. Thabtah,et al.  A review of associative classification mining , 2007, The Knowledge Engineering Review.

[7]  Jian Tang,et al.  Capabilities of outlier detection schemes in large datasets, framework and methodologies , 2006, Knowledge and Information Systems.

[8]  Sanjay Chawla,et al.  CCCS: a top-down associative classifier for imbalanced class distribution , 2006, KDD '06.

[9]  Jianyong Wang,et al.  HARMONY: Efficiently Mining the Best Rules for Classification , 2005, SDM.

[10]  Osmar R. Zaïane,et al.  An associative classifier based on positive and negative rules , 2004, DMKD '04.

[11]  Anthony K. H. Tung,et al.  FARMER: finding interesting rule groups in microarray datasets , 2004, SIGMOD '04.

[12]  Gary M. Weiss Mining with rarity: a unifying framework , 2004, SKDD.

[13]  Damminda Alahakoon,et al.  Minority report in fraud detection: classification of skewed data , 2004, SKDD.

[14]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[15]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[16]  Osmar R. Zaïane,et al.  Text document categorization by term association , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[17]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[18]  Vipin Kumar,et al.  Predicting rare classes: can boosting make any weak learner strong? , 2002, KDD.

[19]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[20]  Vipin Kumar,et al.  Evaluating boosting algorithms to classify rare classes: comparison and improvements , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[21]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[22]  Stephen D. Bay,et al.  Detecting Group Differences: Mining Contrast Sets , 2001, Data Mining and Knowledge Discovery.

[23]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[24]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[25]  Yiming Ma,et al.  Improving an Association Rule Based Classifier , 2000, PKDD.

[26]  Haym Hirsh,et al.  A Quantitative Study of Small Disjuncts , 2000, AAAI/IAAI.

[27]  Kai Ming Ting,et al.  A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.

[28]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[29]  David Wai-Lok Cheung,et al.  Effect of Data Distribution in Parallel Mining of Associations , 1999, Data Mining and Knowledge Discovery.

[30]  Salvatore J. Stolfo,et al.  AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.

[31]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[32]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[33]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[34]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[35]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[36]  Moninder Singh,et al.  Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management , 1996, ICML.

[37]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[38]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.

[39]  Gary M. Weiss Learning with Rare Cases and Small Disjuncts , 1995, ICML.

[40]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[41]  Michael J. Pazzani,et al.  Reducing Misclassification Costs , 1994, ICML.

[42]  R. Mike Cameron-Jones,et al.  FOIL: A Midterm Report , 1993, ECML.

[43]  Robert C. Holte,et al.  Concept Learning and the Problem of Small Disjuncts , 1989, IJCAI.

[44]  Geoffrey I. Webb,et al.  Not So Naive Bayes: Aggregating One-Dependence Estimators , 2005, Machine Learning.

[45]  Jiawei Han,et al.  CPAR: Classification based on Predictive Association Rules , 2003, SDM.

[46]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[47]  Nathalie Japkowicz,et al.  The Class Imbalance Problem: Significance and Strategies , 2000 .

[48]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[49]  Oren Etzioni,et al.  Representation design and brute-force induction in a Boeing manufacturing domain , 1994, Appl. Artif. Intell..

[50]  David G. Stork,et al.  Pattern Classification , 1973 .