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190
Reliable classifiers in ROC space
 in Proceedings of the 15th BENELEARN Machine Learning Conference
, 2006
"... The performance of a classifier can be improved by abstaining on uncertain instance classifications. The transformation from the original Receiver Operator Characteristic (ROC) curve to the curve obtained by abstention is provided. We include proofs on dominance of this new ROC curve to aid class ..."
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Cited by 2 (1 self)
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sifier selection and to show the effectiveness of the approach. For specific cost and class distributions we provide an approach in ROC space to transform a classifier into a new one that has a desired precision per class. 1.
Relevancy constraints revisited in ROC space
"... Abstract. This paper presents relevancy constraints used in subgroup discovery and a novel interpretation of the concept of relevancy in the ROC space context. It provides definitions of feature relevancy and constraints for feature filtering, introduces relevancy based mechanisms for handling of mi ..."
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Abstract. This paper presents relevancy constraints used in subgroup discovery and a novel interpretation of the concept of relevancy in the ROC space context. It provides definitions of feature relevancy and constraints for feature filtering, introduces relevancy based mechanisms for handling
The geometry of ROC space: understanding machine learning metrics through ROC isometrics
 in Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... Many different metrics are used in machine learning and data mining to build and evaluate models. However, there is no general theory of machine learning metrics, that could answer questions such as: When we simultaneously want to optimise two criteria, how can or should they be traded off? Some met ..."
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Cited by 91 (11 self)
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metrics are inherently independent of class and misclassification cost distributions, while other are not — can this be made more precise? This paper provides a derivation of ROC space from first principles through 3D ROC space and the skew ratio, and redefines metrics in these dimensions. The paper
ARTIFICIAL NEURAL NETWORKS LEARNING IN ROC SPACE
"... sensitive learning. Abstract: In order to control the tradeoff between sensitivity and specificity of MLP binary classifiers, we extended the Backpropagation algorithm, in batch mode, to incorporate different misclassification costs via separation of the global mean squared error between positive a ..."
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and negative classes. By achieving different solutions in ROC space, our algorithm improved the MLP classifier performance on imbalanced training sets. In our experiments, standard MLP and SVM algorithms were compared to our solution using real world imbalanced applications. The results demonstrated
The Relationship Between PrecisionRecall and ROC Curves
 In ICML ’06: Proceedings of the 23rd international conference on Machine learning
, 2006
"... Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, PrecisionRecall (PR) curves give a more informative picture of an algorithm’s performance. We show that a deep conn ..."
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Cited by 415 (4 self)
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connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve
SMOTE: Synthetic Minority Oversampling Technique
 Journal of Artificial Intelligence Research
, 2002
"... An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often realworld data sets are predominately composed of ``normal'' examples with only a small percentag ..."
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Cited by 634 (27 self)
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good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of oversampling the minority (abnormal) class and undersampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under
Note On The Location Of Optimal Classifiers In NDimensional ROC Space
, 1999
"... The comparative study of classifier performance is a worthwhile concern in Machine Learning. Empirical comparisons typically examine unbiased estimates of predictive accuracy of different algorithms  the assumption being that the classifier with the highest accuracy would be the "optimal" ..."
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Cited by 36 (1 self)
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The comparative study of classifier performance is a worthwhile concern in Machine Learning. Empirical comparisons typically examine unbiased estimates of predictive accuracy of different algorithms  the assumption being that the classifier with the highest accuracy would be the "optimal" choice of classifier for the problem. The qualification on optimality is needed here, as choice is restricted to the classifiers being compared, and the estimates are typically subject to sampling errors. Comparisons based on predictive accuracy overlook two important practical concerns, namely (a) class distributions cannot be specified precisely. Distribution of classes in the training set are thus rarely matched exactly on new data; and (b) that the costs of different types of errors may be unequal. Using techniques developed in signal detection, Provost and Fawcett describe an elegant method for the comparative assessment of binary classifiers that takes these considerations into account. Thei...
Combining Classifiers in the ROCspace for Offline Signature Verification
"... Abstract: In this work we present a strategy for offline signature verification. It takes into account a writerindependent model which reduces the pattern recognition problem to a 2class problem, hence, makes it possible to build robust signature verification systems even when few signatures per ..."
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Cited by 2 (1 self)
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writer are available. Receiver Operating Characteristic (ROC) curves are used to improve the performance of the proposed system. The contribution of this paper is twofold. First of all, we analyze the impacts of choosing different fusion strategies to combine the partial decisions yielded by the SVM
NSPO has performed the major role of space development in accordance with the Act "Longterm Planning of the ROC Space Technology Development " (The First Phase
"... This paper describes Taiwan’s ROCSATseries space program with the missions and applications in remote sensing as well as in scientific utilization. The concept of costeffective approach in establishing an effective space infrastructure for Taiwan is highly emphasized to realize the space program i ..."
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This paper describes Taiwan’s ROCSATseries space program with the missions and applications in remote sensing as well as in scientific utilization. The concept of costeffective approach in establishing an effective space infrastructure for Taiwan is highly emphasized to realize the space program
Explicitly representing expected cost: an alternative to ROC representation
 KDD
, 2000
"... This paper proposes an alternative to ROC representation, in which the expected cost of a classifier is represented explicitly. This expected cost representation maintains many of the advantages of ROC representation, but is easier to understand. It allows the experimenter to immediately see the ran ..."
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Cited by 93 (10 self)
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the range of costs and class frequencies where a particular classifier is the best and quantitatively how much better it is than other classiers. This paper demonstrates there is a point/line duality between the two representations. A point in ROC space representing a classier becomes a line segment
Results 1  10
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190