Tony Lindgren

Resolving rule conflicts with double induction

When applying an unordered set of classification rules, the rules may assign more than one class to a particular example. Previous methods of resolving such conflicts between rules include using the most frequent class of the examples covered by the conflicting rules (as done in CN2) and using naive Bayes to calculate the most probable class. An alternative way of solving this problem is presented in this paper: by generating new rules from the examples covered by the conflicting rules. These newly induced rules are then used for classification. Experiments on a number of domains show that this method significantly outperforms both the CN2 approach and naive Bayes.

Methods for rule conflict resolution

When using unordered rule sets, conflicts can arise between the rules, i.e., two or more rules cover the same example but predict different classes. This paper gives a survey of methods used to solve this type of conflict and introduces a novel method called Recursive Induction. In total nine methods for resolving rule conflicts are scrutinised. The methods are explained in detail, compared and evaluated empirically on an number of domains. The results show that Recursive Induction outperforms all previously used methods.

On handling conflicts between rules with numerical features

Rule conflicts can arise in machine learning systems that utilise unordered rule sets. A rule conflict is when two or more rules cover the same example but differ in their majority classes. This conflict must be solved before a classification can be made. The standard methods for solving this type of problem are to use naive Bayes to solve the conflict or using the most frequent class (CN2). This paper studies the problem of rule conflicts in the area of numerical features. A novel family of methods, called distance based methods, for solving rule conflicts in continuous domains is presented. An empirical evaluation between a distance based method, CN2 and naive Bayes is made. It is shown that the distance based method significantly outperforms both naive Bayes and CN2.

Classification with Intersecting Rules

Several rule induction schemes generate hypotheses in the form of unordered rule sets. One important problem that has to be addressed when classifying examples with such hypotheses is how to deal with overlapping rules that predict different classes. Previous approaches to this problem calculate class probabilities based on the union of examples covered by the overlapping rules (as in CN2) or assumes rule independence (using naive Bayes). It is demonstrated that a significant improvement in accuracy can be obtained if class probabilities are calculated based on the intersection of the overlapping rules, or in case of an empty intersection, based on as few intersecting regions as possible.

Open government ideologies in post-soviet countries

Most research in research areas like e-government, e-participation and open government assumes a democratic norm. The open government (OG) concept is commonly based on a general liberal and deliberative ideology emphasising transparency, access, participation and collaboration, but also innovation and accountability are promoted. In this paper, we outline a terminology and suggest a method for how to investigate the concept more systematically in different policy documents, with a special emphasis on post-soviet countries. The result shows that the main focus in this regions OG policy documents is on freedom of information and accountability, and to a lesser extent on collaboration, while other aspects, such as diversity and innovation, are more rarely mentioned, if at all.

Random Rule Sets – Combining Random Covering with the Random Subspace Method

Ensembles of classifiers has proven itself to be among the best methods for creating highly accurate prediction models. In this paper we combine the random coverage method which facilitates additio ...

Randomized Separate and Conquer Rule induction

Rule learning comes in many forms, here we investigate a modified version of Separate and Conquer (SAC) learning to see if it improves the predictive performance of the induced predictive models. Our modified version of SAC has a hyperparameter which is used to specify the amount of examples that should not be removed from the induction. This selection is done at random and as a consequence the SAC algorithm will produce more and diverse rules, given the hyperparameter setting. The modified algorithm has been implemented in both an unordered single rule set setting as well as in an ensemble rule set setting. Both of these settings have been evaluated empirically on a number of datasets. The results show that in the single rule set setting, the modified version significantly improves the predictive performance, at the cost of more rules, which was expected. In the ensemble setting the combined method of bagging and the modified SAC algorithm did not perform as good as expected, while using only the modified SAC algorithm in ensemble setting performed better than expected.

Indexing rules in rule sets for fast classification

Using sets of rules for classification of examples usually involves checking a number of conditions to see if they hold or not. If the rule set is large the time to make the classification can be lengthy. In this paper we propose an indexing algorithm to decrease the classification time when dealing with large rule sets. Unordered rule sets have a high time complexity when conducting classification; we hence conduct experiments comparing our novel indexing algorithm with the standard way of classifying ensembles of unordered rule sets. The result of the experiment shows decreased classification times for the novel method that are ranging from 0.6 to 0.8 of that of the standard approach averaged over all experimental datasets. This time gain is obtained while retaining an accuracy ranging from 0.84 to 0.99 with regard to the standard classification method. The index bit size used with the indexing algorithm influence both the classification accuracy and time needed for conducting the classification task.

Model Based Sampling - Fitting an Ensemble of Models into a Single Model

Large ensembles of classifiers usually outperform single classifiers. Unfortunately ensembles have two major drawbacks compared to single classifier, interpretability and classifications times. Using the Combined Multiple Models (CMM) framework for compressing an ensemble of classifiers into a single classifier the problems associated with ensembles can be avoided while retaining almost similar classification power as that of the original ensemble. One open question when using CMM concerns how to generate values that constitute the synthetic example. In this paper we present a novel method for generating synthetic examples by utilizing the structure of the ensemble. This novel method is compared with other methods for generating synthetic examples using the CMM framework. From the comparison it is concluded that the novel method outperform the other methods.

Troubleshooting ECU Programmed by Bodybuilders

Having an Electronic Control Unit (ECU) which is programmable by external parties puts new requirements on troubleshooting. In this paper we describe how one could solve the problems of both troubleshooting additional equipment added by bodybuilders and facilitating their need to use signals from vehicles in an easy way in order to interact with their additional equipment. In this paper we look at bodybuilders additional equipment for heavy trucks, but our technique for troubleshooting should be equally relevant for other applications with similar conditions.