Elements of information theory
Elements of information theory
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
Estimation of False Negatives in Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Relationship between diversity and correlation in multi-classifier systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Data mining is increasingly being applied in environments having very high rate of data generation like network intrusion detection [7], where routers generate about 300,000 -- 500,000 connections every minute. In such rare class data domains, the cost of missing a rare-class instance is much higher than that of other classes. However, the high cost for manual labeling of instances, the high rate at which data is collected as well as real-time response constraints do not always allow one to determine the actual classes for the collected unlabeled datasets. In our previous work [9], this problem of missed false negatives was explained in context of two different domains -- "network intrusion detection" and "business opportunity classification". In such cases, an estimate for the number of such missed high-cost, rare instances will aid in the evaluation of the performance of the modeling technique (e.g. classification) used. A capture-recapture method was used for estimating false negatives, using two or more learning methods (i.e. classifiers). This paper focuses on the dependence between the class labels assigned by such learners. We define the conditional independence for classifiers given a class label and show its relation to the conditional independence of the features sets (used by the classifiers) given a class label. The later is a computationally expensive problem and hence, a heuristic algorithm is proposed for obtaining conditionally independent (or less dependent) feature sets for the classifiers. Initial results of this algorithm on synthetic datasets are promising and further research is being pursued.