C. E. Brodley. Addressing the selective superiority problem: Automatic algorithm / model class selection. In International Conference on Machine Learning, pages 17--24, 1993.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....flexible analysis strategies and a variety of representations are more proficient at uncovering structure in data. There are several reasons to suspect this is true. First, in having several techniques to choose from, a system can select the one most suited to the analysis problem at hand (e.g. [2]) Second, integrated systems have the advantage of supplementing one kind of result with others, information which often constitutes an explanation of previous findings (e.g. 7] Finally,interesting structure is often discovered in the process of shifting from one representation to another ....

Carla E. Brodley. Addressing the selective superiority problem: Automatic algorithm /model class selection. In Proceedings of the Tenth International Machine Learning Conference, pages 17--24, 1993.

....the utility of the diversity, the coverage and the class specialty properties of a candidate set of classifiers as alternate metrics that capture additional information for the better analysis and understanding of the characteristics of that set of classifiers. 5.2. 1 Diversity Brodley [ C.Brodley, 1993 ] defines diversity by measuring the classification overlap of a pair of classifiers, i.e. the percentage of the instances classified the same way by two classifiers while Chan [ Chan, 1996 ] associates it with the entropy in the set of predictions of the base classifiers. When the predictions ....

C.Brodley. 1993. Addressing the selective superiority problem: Automatic algorithm/model class selection. In Proc. 10th Intl. Conf. Machine Learning, 17--24. Morgan Kaufmann.

....error as the fraction of instances for which a pair of base classifiers make the same incorrect predictions and Brodley and Lane [5] measured coverage by computing the fraction of instances for which at least one of the base classifiers produces the correct prediction. 3. 1 Diversity Brodley [6] defines diversity by measuring the classification overlap of a pair of classifiers, i.e. the percentage of the instances classified the same way by two classifiers while Chan [7] associates it with the entropy in the set of predictions of the base classifiers. When the predictions of the ....

C.Brodley. Addressing the selective superiority problem: Automatic algorithm/model class selection. In Proc. 10th Intl. Conf. Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

....Similar work is reported by Brazdil (1998) who proposes using meta learning as a pre processing step to model selection; experimentation on accuracy performance is then used to select the best algorithm. Meta rules matching domain characteristics with inductive bias have also been crafted manually (Brodley, 1993; Brodley, 1994) In addition, a domain may be represented by properties of the nal hypothesis rather than the data itself. For example, Bensusan et. al (2000) measure properties of a decision tree, e.g. nodes per feature, maximum tree depth, shape, tree imbalance, etc. and convert them into ....

Brodley Carla (1993). Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection. Proceedings of the Tenth International Conference on Machine Learning, 17-24, San Mateo, CA, Morgan Kaufman.

....them after their inclusion in the case base. Stored cases that miss classify to a statistically significant degree are removed. Note that these cases could also be useful exceptions to the class definitions. A number of workers have augmented the IBn algorithms (Cameron Jones, 1992; Zhang, 1992; Brodley, 1993). To summarise, Aha s algorithms offer an incremental approach to filtering, and for this reason offer improved efficiency, but suffer from the order of case presentation. Crucial cases could be rejected early on when the class definitions are poorly defined. Wilson and Martinez present three ....

Brodley, C. (1993). Addressing the selective superiority problem: Automatic algorithm/mode class selection. In Proceedings of the Tenth International Machine Learning Conference, pages 17 -- 24, Amherst, MA.

....to Spearman s correlation coefficient that could be tried is Kendall s tau [16] 6 Related Work Meta knowledge as been used before for the purpose of algorithm selection. This knowledge can be either of theoretical or of experimental origin, or a mixture of both. The rules described by Brodley [6] for instance, captured the knowledge of experts concerning the applicability of certain classification algorithms. The meta knowledge of [1] 4] and [9] was of experimental origin and was obtained by meta learning on past performance information of the algorithms. Its objective is to capture ....

C.E. Brodley. Addressing the selective superiority problem: Automatic Algorithm /Model class selection. In P. Utgoff, editor, Proceedings of the Tenth International Conference on Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

....to Spearman s correlation coefficient that could be tried is Kendall s tau [16] 6 Related Work Meta knowledge as been used before for the purpose of algorithm selection. This knowledge can be either of theoretical or of experimental origin, or a mixture of both. The rules described by Brodley [6] for instance, captured the knowledge of experts concerning the applicability of certain classification algorithms. The meta knowledge of [1] 4] and [9] was of experimental origin and was obtained by meta learning on past performance information of the algorithms. Its objective is to capture ....

C.E. Brodley. Addressing the selective superiority problem: Automatic Algorithm /Model class selection. In P. Utgoff, editor, Proceedings of the Tenth International Conference on Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

....in the problem of algorithm selection based on past performance is growing 6 . Most recent approaches exploited Meta knowledge concerning the performance of algorithms. This knowledge can be either theoretical or of experimental origin, or a mixture of both. The rules described by Brodley [5] captured the knowledge of experts concerning the applicability of certain classification algorithms. Most often, the meta knowledge is of experimental origin [1, 4, 10, 11, 18] In the analysis of the results of project StatLog [12] the objective of the meta knowledge is to capture certain ....

C.E. Brodley. Addressing the selective superiority problem: Automatic Algorithm /Model class selection. In P. Utgoff, editor, Proceedings of the 10th International Conference on Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

....to Spearman s correlation coefficient that could be tried is Kendall s tau [16] 6 Related Work Meta knowledge as been used before for the purpose of algorithm selection. This knowledge can be either of theoretical or of experimental origin, or a mixture of both. The rules described by Brodley [6] for instance, captured the knowledge of experts concerning the applicability of certain classification algorithms. The meta knowledge of [1] 4] and [9] was of experimental origin and was obtained by meta learning on past performance information of the algorithms. Its objective is to capture ....

C.E. Brodley. Addressing the selective superiority problem: Automatic Algorithm /Model class selection. In P. Utgoff, editor, Proceedings of the 10th International Conference on Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

....difficulties are compensated for by a number of important advances in areas relevant to KDD. Here we list only a few: Multistrategy systems: Several recent comparisons of different learning and discovery algorithms have showed that different methods are superior for different types of problems (Brodley 1993); no single method is best across a range of problems. As a result, there is a movement toward multistrategy learning methods, especially for classification, that apply a number of different methods to the same task and select rules from the best method. Multistrategy systems is an area of active ....

Brodley, C. 1993. Addressing the Selective Superiority Problem: Automatic Algorithm -Model Class Selection. In Proceedings of the Tenth Machine Learning Conference, 17--24. San Mateo, Calif.: Morgan Kaufmann.

.... of these other steps is a matter of art rather than a routine allowing automation [8] Lately some e#orts in the KDD field have been directed towards intelligent support to the data mining process, in particular, assistance in the selection of an analysis method depending on data characteristics [2,4]. A particular case of KDD is knowledge extraction from spatially referenced data, i.e. data referring to geographic objects or locations or parts of a territory division. In analysis of such data it is very important to account for the spatial component (relative positions, adjacency, distances, ....

Brodley, C.: Addressing the Selective Superiority Problem: Automatic Algorithm / Model Class Selection. In: Machine Learning: Proceedings of the 10th International Conference, University of Massachusetts, Amherst, June 27-29, 1993. San Mateo, Calif.: Morgan Kaufmann (1993) 17-24

....expectations of the modern cognitivists. Concurrently to these cognitive motivations, many works in the field of inductive learning have highlighted the relation between biases implemented in the learning algorithms and their efficiency to infer correct hypothesis [Utgoff, 1986; Schaffer, 1993, Brodley, 1993]. For example, it has been shown that the orthogonality of class boundaries is often a too strong constraint that might deeply affect the accuracy and the simplicity of the decision surfaces drawn in the description space. Techniques like oblique decision trees [Murthy and al. 1994] or perceptron ....

.... It is the deep knowledge level which results from an inductive learning process whose biases focus on accuracy of the concept representation. These biases may include any kind of Background Knowledge that helps the system to choose a specific knowledge model regarding the domain (as done by [Brodley, 1993]) or that encodes a priori domain theory that generates constraints on the possible concept representations. The resulting representation, called the Internal Concept Representation (ICR) is further used by a classifying function which aims to recognize instances from non instances of the ....

Brodley Carla E. (1993) Addressing the Selective Superiority Problem: Automatic Algorithm/ Model Class Selection, Proceedings of the Tenth International Conference on Machine Learning ML'93, Morgan Kaufmann, pp. 17-24.

....measure coverage by computing the fraction of instances for which at least one of the classifiers produces the correct prediction. 1 TP stands for True Positive, i.e. percentage of actual fraud that is caught, FP stands for False Positive, i.e. percentage of false alarms 2. 1 Diversity Brodley [5] defines diversity by measuring the classification overlap of a pair of classifiers, i.e. the percentage of the instances classified the same way by two classifiers, while Chan [6] associates it with the entropy in the predictions of the base classifiers. When the predictions of the classifiers ....

C.Brodley. Addressing the selective superiority problem: Automatic algorithm/model class selection. 10th Intl. Conf. Mach. Learning, 1993.

....in Table 3. The highest accuracy achieved for each dataset is shown in bold type. The average over all datasets is shown near the bottom of the table. As can be seen from the results in the table, no algorithm had the highest accuracy on all of the datasets, due to the selective superiority (Brodley 1993) of each algorithm, i.e. the degree to which each bias (Mitchell 1980) is appropriately matched for each dataset (Dietterich 1989; Wolpert 1993; Schaffer 1994; Wilson Martinez 1997c) However, IDIBL had the highest accuracy of any of the algorithms for more of the datasets than any of the other ....

Brodley, Carla E. 1993. Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection. Proceedings of the Tenth International Machine Learning Conference, Amherst, MA, pp. 17-24.

....into different categories or classes. Classification has been the subject of much study in the past, and has proved a useful tool in real world applications. Nevertheless, the reasons explaining why an algorithm is more successful than others in giving good approximations to C remain elusive (Brodley, 1993). To elucidate the problem above, experiments employing a broad spectrum of learning algorithms have been sought to find the class of domains for which each algorithm works best (Michie, 1994; Weiss Kulikowski, 1990) The idea is to trace a link between a learning algorithm and the domains on ....

Brodley, C. E. (1993). Addressing the selective superiority problem: Automatic algorithm/model class selection. In Proceedings of the Tenth International Conference on Machine Learning, pp.

....several other meta learning strategies. 1.1 Introduction Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain (e.g. Aha92, Breiman84] or for a portion of the domain ([Brodley93, Brodley94]) have met with limited success. This paper proposes a new approach that dynamically selects a learning algorithm for each example by locating it in the example space and then choosing the best learner(s) for prediction in that part of the example space. The regions of the example space are ....

....is often difficult to obtain. DS DW emphasizes the selection of a learning algorithm at the example level rather than at the domain level since it avoids the need for characterization rules by instead considering each learning algorithm s past performance on similar examples from that domain. [Brodley93] proposes a knowledge based approach to building hybrid decision structures by using heuristics to select the best algorithm at a given stage of learning. These heuristics are created from practical knowledge about how to detect when a generalization is a good fit or when to switch to a different ....

Brodley, C. E. (1993). Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection. Proceedings of the Tenth International Machine Learning Conference (pp. 17-24). San Mateo, CA: Morgan Kaufmann.

....error the fraction of instances for which a pair of base classifiers make the same incorrect predictions and Brodley and Lane [4] measured coverage by computing the fraction of instances for which at least one of the base classifiers produces the correct prediction. 2. 1 Diversity Brodley [5] defines diversity by measuring the classification overlap of a pair of classifiers, i.e. the percentage of the instances classified the same way by two classifiers while Chan [6] associates it with the entropy in the predictions of the base classifiers. When the predictions of the classifiers ....

C.Brodley. Addressing the selective superiority problem: Automatic algorithm/model class selection. In Proc. 10th Intl. Conf. Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

....classifier can be used, and we are currently developing recognition graphs that use artificial neural networks and multivariate decision trees instead. One could also build a hybrid system that selected the best classification induction algorithm for each level of abstraction, a la Brodley [7]. It is important, however, that the classifier have a very low false negative rate. At all but the highest level of abstraction, if a classifier verifies a false instance the mistake will most likely be corrected when the instance is transformed to the next level of representation. If a ....

C.E. Brodley. "Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection," Proc. of Tenth International Machine Learning Conference, June 27-29, Amherst, MA., pp. 17-24.

....error as the fraction of instances for which a pair of base classifiers make the same incorrect predictions and Brodley and Lane [5] measured coverage by computing the fraction of instances for which at least one of the base classifiers produces the correct prediction. 3. 1 Diversity Brodley [6] defines diversity by measuring the classification overlap of a pair of classifiers, i.e. the percentage of the instances classified the same way by two classifiers while Chan [7] associates it with the entropy in the set of predictions of the base classifiers. When the predictions of the ....

C.Brodley. Addressing the selective superiority problem: Automatic algorithm/model class selection. In Proc. 10th Intl. Conf. Machine Learning, pages 17--24. Morgan Kaufmann, 1993.

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C. Brodley. Addressing the selective superiority problem: Automatic algorithm/model class selection. In Proceedings of the Tenth International Conference on Machine Learning, pages 17--24, 1993.

.... decision tree (Quinlan 1986) instance based (Duda Hart 1973) and neural net al..gorithms (Rumelhart McClelland 1986) The results of empirical comparisons of existing algorithms illustrate that each algorithm has a selective superiority: it is best for some but not all classification tasks (Brodley 1993). Selective superiority arises because each learning algorithm searches within a restricted hypothesis space defined by its class of models. For example, the class of first order linear regression models is not appropriate when the data is best fit by a second order linear regression model. In ....

Brodley, C. E. 1993. Addressing the selective superiority problem: Automatic algorithm/model class selection. In Machine Learning: Proceedings of the Tenth International Conference, 17--24, Morgan Kaufmann.

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C. E. Brodley. Addressing the selective superiority problem: Automatic algorithm / model class selection. In International Conference on Machine Learning, pages 17--24, 1993.

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C.E. Brodley. Addressing the selective superiority problem: Automatic algorithm for model class selection. In Proceedings 10th Machine Learning Conference, pages 17--24, 1993.

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C. E. Brodley. Addressing the selective superiority problem: Automatic algorithm /model class selection. In Proceedings of the Tenth International Conference on Machine Learning, pages 17--24, Amherst, MA, 1993.

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C. E. Brodley. Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection. In Proceedings of the Tenth International Machine Learning Conference, pages 17--24. Amherst, MA, 1993.

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