| 1. |
- Johansson, Ulf, et al.
(författare)
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Using Imaginary Ensembles to Select GP Classifiers
- 2010
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Ingår i: Genetic Programming : 13th European Conference, EuroGP 2010, Istanbul, Turkey, April 7-9, 2010. Proceedings. - Springer Berlin/Heidelberg. - 978-3-642-12147-0 (Print) - 978-3-642-12148-7 (Online) - 3-642-12147-0 ; s. 278-288
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Konferensbidrag (refereegranskat)abstract
- When predictive modeling requires comprehensible models, most data miners will use specialized techniques producing rule sets or decision trees. This study, however, shows that genetically evolved decision trees may very well outperform the more specialized techniques. The proposed approach evolves a number of decision trees and then uses one of several suggested selection strategies to pick one specific tree from that pool. The inherent inconsistency of evolution makes it possible to evolve each tree using all data, and still obtain somewhat different models. The main idea is to use these quite accurate and slightly diverse trees to form an imaginary ensemble, which is then used as a guide when selecting one specific tree. Simply put, the tree classifying the largest number of instances identically to the ensemble is chosen. In the experimentation, using 25 UCI data sets, two selection strategies obtained significantly higher accuracy than the standard rule inducer J48.
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| 2. |
- König, Rikard, et al.
(författare)
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Instance Ranking Using Ensemble Spread
- 2007
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Ingår i: 2007 International Conference on Data Mining, DMIN. - CSREA Press. - 1-60132-031-0 ; s. 73-78
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Konferensbidrag (refereegranskat)abstract
- This paper investigates a technique for predicting ensemble uncertainty originally proposed in the weather forecasting domain. The overall purpose is to find out if the technique can be modified to operate on a wider range of regression problems. The main difference, when moving outside the weather forecasting domain, is the lack of extensive statistical knowledge readily available for weather forecasting. In this study, three different modifications are suggested to the original technique. In the experiments, the modifications are compared to each other and to two straightforward technniques, using ten publicly available regression problems. Three of the techniques show promising result, especially one modification based on genetic algorithms. The suggested modification can accurately determine whether the confidence in ensemble predictions should be high or low.
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| 3. |
- König, Rikard
(författare)
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Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality
- 2009
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Licentiatavhandling (övrigt vetenskapligt)abstract
- Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. There are numerous predictive techniques, ranging from simple techniques such as linear regression, to complex powerful ones like artificial neural networks. Complex models usually obtain better predictive performance, but are opaque and thus cannot be used to explain predictions or discovered patterns. The design choice of which predictive technique to use becomes even harder since no technique outperforms all others over a large set of problems. It is even difficult to find the best parameter values for a specific technique, since these settings also are problem dependent. One way to simplify this vital decision is to combine several models, possibly created with different settings and techniques, into an ensemble. Ensembles are known to be more robust and powerful than individual models, and ensemble diversity can be used to estimate the uncertainty associated with each prediction.In real-world data mining projects, data is often imprecise, contain uncertainties or is missing important values, making it impossible to create models with sufficient performance for fully automated systems. In these cases, predictions need to be manually analyzed and adjusted. Here, opaque models like ensembles have a disadvantage, since the analysis requires understandable models. To overcome this deficiency of opaque models, researchers have developed rule extraction techniques that try to extract comprehensible rules from opaque models, while retaining sufficient accuracy.This thesis suggests a straightforward but comprehensive method for predictive modeling in situations with poor data quality. First, ensembles are used for the actual modeling, since they are powerful, robust and require few design choices. Next, ensemble uncertainty estimations pinpoint predictions that need special attention from a decision maker. Finally, rule extraction is performed to support the analysis of uncertain predictions. Using this method, ensembles can be used for predictive modeling, in spite of their opacity and sometimes insufficient global performance, while the involvement of a decision maker is minimized.The main contributions of this thesis are three novel techniques that enhance the performance of the purposed method. The first technique deals with ensemble uncertainty estimation and is based on a successful approach often used in weather forecasting. The other two are improvements of a rule extraction technique, resulting in increased comprehensibility and more accurate uncertainty estimations.
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| 4. |
- König, Rikard, et al.
(författare)
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Using Genetic Programming to Increase Rule Quality
- 2008
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Ingår i: Proceedings of the Twenty-First International FLAIRS Conference (FLAIRS 2008). - AAAI Press. - 978-1-57735-365-2 ; s. 288-293
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Konferensbidrag (refereegranskat)abstract
- Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness functions for G-REX and to show how G-REX can be used as a rule inducer. The fitness functions are designed to optimize two alternative quality measures, area under ROC curves and a new comprehensibility measure called brevity. Rules with good brevity classifies typical instances with few and simple tests and use complex conditions only for atypical examples. Experiments using thirteen publicly available data sets show that the two novel fitness functions succeeded in increasing brevity and area under the ROC curve without sacrificing accuracy. When compared to a standard decision tree algorithm, G-REX achieved slightly higher accuracy, but also added additional quality to the rules by increasing their AUC or brevity significantly.
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