L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Effective and Efficient Pruning of Meta-Classifiers in a .. - Prodromidis, Stolfo.. (1998) (Correct)
....accuracy, cost, etc. The intent of these algorithms is to discover and prune away the suboptimal models, while in this work, we focus on evaluation metrics that provide information about the interdependencies among the (base) classifiers and their potential when forming ensembles of classifiers [10, 14]. In fact, the performance of sub optimal yet diverse models can be substantially improved when combined together and even surpass that of the best single model. In other related work, Margineantu and Dietterich [20] studied the problem of pruning the ensemble of classifiers (i.e. the set of ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
Pruning Meta-Classifiers in a Distributed Data Mining System - Prodromidis, Stolfo (1998) (7 citations) (Correct)
....accuracy, cost, etc. The intent of these algorithms is to discover and prune away the sub optimal models, while in this work, we focus on evaluation metrics that provide information about the interdependencies among the (base) classifiers and their potential when forming ensembles of classifiers [10, 12]. In fact, the performance of sub optimal yet diverse models can be substantially improved when combined together and even surpass that of the best single models. In other related work, Margineantu and Dietterich [15] studied the problem of pruning the ensemble of classifiers (i.e. the set of ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
Meta-Learning in Distributed Data Mining Systems: Issues.. - Prodromidis, Chan, al. (2000) (34 citations) (Correct)
....cost, etc. The intent of these algorithms, however, is to select the best classifier (not a group of classifiers) under a specific performance criterion (which could be adjusted in the ROC space) In this work, the focus is on methods with the potential to form effective ensembles of classifiers [19, 27]. In fact, the performance of sub optimal yet diverse models can be substantially improved when combined together and even surpass that of the best single model. Margineantu and Dietterich [38] studied the problem of pruning the ensemble of classifiers (i.e. the set of hypothesis (classifiers) ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
Pruning Meta-Classifiers in a Distributed Data Mining System - Prodromidis, Stolfo (1998) (7 citations) (Correct)
....(TP FP rates, accuracy, cost, etc. The focus of this paper, however, is on evaluation methods that are suitable for multi class problems and on metrics that provide information about the interdependencies among the base classifiers and their potential when forming ensembles of classifiers [10, 14]. In the most related work, Margineantu and Dietterich [18] studied the problem of pruning the ensemble of classifiers (i.e. the set of hypothesis (classifiers) obtained by the boosting algorithm ADABOOST [13] According to their findings, by examining the diversity and accuracy of the available ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993-- 1001, 1990.
Effective and Efficient Pruning of Meta-Classifiers in a.. - Prodromidis, Stolfo (1999) (Correct)
....accuracy, cost, etc. The intent of these algorithms is to discover and prune away the suboptimal models, while in this work, we focus on evaluation metrics that provide information about the interdependencies among the (base) classifiers and their potential when forming ensembles of classifiers [10, 14]. In fact, the performance of sub optimal yet diverse models can be substantially improved when combined together and even surpass that of the best single model. In other related work, Margineantu and Dietterich [20] studied the problem of pruning the ensemble of classifiers (i.e. the set of ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
Pruning Meta-Classifiers in a Distributed Data Mining System - Prodromidis, Stolfo (1998) (7 citations) (Correct)
....accuracy, cost, etc. The intent of these algorithms is to discover and prune away the sub optimal models, while in this work, we focus on evaluation metrics that provide information about the interdependencies among the (base) classifiers and their potential when forming ensembles of classifiers [10, 12]. In fact, the performance of sub optimal yet diverse models can be substantially improved when combined together and even surpass that of the best single models. In other related work, Margineantu and Dietterich [15] studied the problem of pruning the ensemble of classifiers (i.e. the set of ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
Meta-Learning in Distributed Data Mining Systems: Issues.. - Prodromidis, Chan, al. (2000) (34 citations) (Correct)
....cost, etc. The intent of these algorithms, however, is to select the best classifier (not a group of classifiers) under a specific performance criterion (which could be adjusted in the ROC space) In this work, the focus is on methods with the potential to form e#ective ensembles of classifiers [17, 24]. In fact, the performance of sub optimal yet diverse models can be substantially improved when combined together and even surpass that of the best single model. Margineantu and Dietterich [32] studied the problem of pruning the ensemble of classifiers (i.e. the set of hypothesis (classifiers) ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
Pruning Classifiers in a Distributed Meta-Learning System - Prodromidis, Stolfo, Chan (1998) (3 citations) (Correct)
....negative rates, accuracy, cost, etc. The focus of this paper, however, is on evaluation methods that are suitable for multiple class problems and on metrics that provide information about the interdependencies among the base classifiers and their potential when forming ensembles of classifiers [9, 13]. Margineantu and Dietterich [15] acknowledged the importance of pruning ensembles of classifiers and studied the problem of evaluating and pruning the set of hypothesis (classifiers) obtained by the boosting algorithm ADABOOST [11] According to their findings, by examining the diversity and ....
L. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Analysis and Mach. Itell., 12:993--1001, 1990.
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