Improving XCS performance on overlapping binary problems

Ioannides, Charalambos; Barrett, Geoff; Eder, Kerstin

doi:10.1109/cec.2011.5949782

“…It is observed that standard XCS achieved approximately 97% performance level, but could not completely solve the DV1 problem, whereas the XCSCFA methods have successfully solved it. Ioannides et al [9] improved the performance of XCS in the DV1 problem to 99.76%, by modifying the standard fitness update procedure and using an individually computed learning rate for each classifier, but still could not completely solve it.…”

Section: The Design Verification Problem Domainmentioning

confidence: 98%

Comparison of two methods for computing action values in XCS with code-fragment actions

Iqbal

¹

,

Browne

²

,

Zhang

³

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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XCS is a learning classifier system that uses accuracy-based fitness to learn a problem. Commonly, a classifier rule in XCS is encoded using a ternary alphabet based condition and a numeric action. Previously, we implemented a codefragment action based XCS, called XCSCFA, where the typically used numeric action was replaced by a genetic programming like tree-expression. In XCSCFA, the action value in a classifier was computed by loading the terminal symbols in the action-tree with the corresponding binary values in the condition of the classifier rule. This enabled accurate, general and compact rule sets to be simply produced. The main contribution of this work is to investigate an intuitive way, i.e. using the environmental instance, to compute the action value in XCSCFA, instead of the condition of the classifier rule. The methods will be compared in five different Boolean problem domains, i.e. multiplexer, even-parity, majority-on, design verification, and carry problems. The environmental instance based XCSCFA approach had better classification performance than standard XCS as well as classifier condition based XCSCFA and solved all the problems experimented here. In addition it produced more general and compact classifier rules in the final solution. However, classifier condition based XCSCFA has the advantage of producing the optimal classifiers such that they are clearly separated from the sub-optimal ones in certain domains.

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“…Various richer encoding schemes have been investigated to represent high level knowledge in LCS in an attempt to obtain compact classifier rules [31,62,63], to solve overlapping problems [59,64], to approximate functions [19,139], to develop useful feature extractors [6], to tackle problems involving large number of discrete actions [96,84], to compute continuous actions [140,129,61], and to identify and process building blocks of knowledge [20,60].…”

Section: Learning Classifier Systemsmentioning

confidence: 99%

Learning Feature Selection and Combination Strategies for Generic Salient Object Detection

Naqvi¹

0

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<p>For a diverse range of applications in machine vision from social media searches to robotic home care providers, it is important to replicate the mechanism by which the human brain selects the most important visual information, while suppressing the remaining non-usable information. Many computational methods attempt to model this process by following the traditional model of visual attention. The traditional model of attention involves feature extraction, conditioning and combination to capture this behaviour of human visual attention. Consequently, the model has inherent design choices at its various stages. These choices include selection of parameters related to the feature computation process, setting a conditioning approach, feature importance and setting a combination approach. Despite rapid research and substantial improvements in benchmark performance, the performance of many models depends upon tuning these design choices in an ad hoc fashion. Additionally, these design choices are heuristic in nature, thus resulting in good performance only in certain settings. Consequentially, many such models exhibit low robustness to difficult stimuli and the complexities of real-world imagery. Machine learning and optimisation technique have long been used to increase the generalisability of a system to unseen data. Surprisingly, artificial learning techniques have not been investigated to their full potential to improve generalisation of visual attention methods. The proposed thesis is that artificial learning can increase the generalisability of the traditional model of visual attention by effective selection and optimal combination of features. The following new techniques have been introduced at various stages of the traditional model of visual attention to improve its generalisation performance, specifically on challenging cases of saliency detection: 1. Joint optimisation of feature related parameters and feature importance weights is introduced for the first time to improve the generalisation of the traditional model of visual attention. To evaluate the joint learning hypothesis, a new method namely GAOVSM is introduced for the tasks of eye fixation prediction. By finding the relationships between feature related parameters and feature importance, the developed method improves the generalisation performance of baseline method (that employ human encoded parameters). 2. Spectral matting based figure-ground segregation is introduced to overcome the artifacts encountered by region-based salient object detection approaches. By suppressing the unwanted background information and assigning saliency to object parts in a uniform manner, the developed FGS approach overcomes the limitations of region based approaches. 3. Joint optimisation of feature computation parameters and feature importance weights is introduced for optimal combination of FGS with complementary features for the first time for salient object detection. By learning feature related parameters and their respective importance at multiple segmentation thresholds and by considering the performance gaps amongst features, the developed FGSopt method improves the object detection performance of the FGS technique also improving upon several state-of-the-art salient object detection models. 4. The introduction of multiple combination schemes/rules further extends the generalisability of the traditional attention model beyond that of joint optimisation based single rules. The introduction of feature composition based grouping of images, enables the developed IGA method to autonomously identify an appropriate combination strategy for an unseen image. The results of a pair-wise ranksum test confirm that the IGA method is significantly better than the deterministic and classification based benchmark methods on the 99% confidence interval level. Extending this line of research, a novel relative encoding approach enables the adapted XCSCA method to group images having similar saliency prediction ability. By keeping track of previous inputs, the introduced action part of the XCSCA approach enables learning of generalised feature importance rules. By more accurate grouping of images as compared with IGA, generalised learnt rules and appropriate application of feature importance rules, the XCSCA approach improves upon the generalisation performance of the IGA method. 5. The introduced uniform saliency assignment and segmentation quality cues enable label free evaluation of a feature/saliency map. By accurate ranking and effective clustering, the developed DFS method successfully solves the complex problem of finding appropriate features for combination (on an-image-by-image basis) for the first time in saliency detection. The DFS method enables ground truth free evaluation of saliency methods and advances the state-of-the-art in data driven saliency aggregation by detection and deselection of redundant information. The final contribution is that the developed methods are formed into a complete system where analysis shows the effects of their interactions on the system. Based on the saliency prediction accuracy versus computational time trade-off, specialised variants of the proposed methods are presented along with the recommendations for further use by other saliency detection systems. This research work has shown that artificial learning can increase the generalisation of the traditional model of attention by effective selection and optimal combination of features. Overall, this thesis has shown that it is the ability to autonomously segregate images based on their types and subsequent learning of appropriate combinations that aid generalisation on difficult unseen stimuli.</p>

show abstract

“…Various richer encoding schemes have been investigated to represent high level knowledge in LCS in an attempt to obtain compact classifier rules [31,62,63], to solve overlapping problems [59,64], to approximate functions [19,139], to develop useful feature extractors [6], to tackle problems involving large number of discrete actions [96,84], to compute continuous actions [140,129,61], and to identify and process building blocks of knowledge [20,60].…”

Section: Learning Classifier Systemsmentioning

confidence: 99%

Learning Feature Selection and Combination Strategies for Generic Salient Object Detection

Naqvi¹

0

View full text Add to dashboard Cite

<p>For a diverse range of applications in machine vision from social media searches to robotic home care providers, it is important to replicate the mechanism by which the human brain selects the most important visual information, while suppressing the remaining non-usable information. Many computational methods attempt to model this process by following the traditional model of visual attention. The traditional model of attention involves feature extraction, conditioning and combination to capture this behaviour of human visual attention. Consequently, the model has inherent design choices at its various stages. These choices include selection of parameters related to the feature computation process, setting a conditioning approach, feature importance and setting a combination approach. Despite rapid research and substantial improvements in benchmark performance, the performance of many models depends upon tuning these design choices in an ad hoc fashion. Additionally, these design choices are heuristic in nature, thus resulting in good performance only in certain settings. Consequentially, many such models exhibit low robustness to difficult stimuli and the complexities of real-world imagery. Machine learning and optimisation technique have long been used to increase the generalisability of a system to unseen data. Surprisingly, artificial learning techniques have not been investigated to their full potential to improve generalisation of visual attention methods. The proposed thesis is that artificial learning can increase the generalisability of the traditional model of visual attention by effective selection and optimal combination of features. The following new techniques have been introduced at various stages of the traditional model of visual attention to improve its generalisation performance, specifically on challenging cases of saliency detection: 1. Joint optimisation of feature related parameters and feature importance weights is introduced for the first time to improve the generalisation of the traditional model of visual attention. To evaluate the joint learning hypothesis, a new method namely GAOVSM is introduced for the tasks of eye fixation prediction. By finding the relationships between feature related parameters and feature importance, the developed method improves the generalisation performance of baseline method (that employ human encoded parameters). 2. Spectral matting based figure-ground segregation is introduced to overcome the artifacts encountered by region-based salient object detection approaches. By suppressing the unwanted background information and assigning saliency to object parts in a uniform manner, the developed FGS approach overcomes the limitations of region based approaches. 3. Joint optimisation of feature computation parameters and feature importance weights is introduced for optimal combination of FGS with complementary features for the first time for salient object detection. By learning feature related parameters and their respective importance at multiple segmentation thresholds and by considering the performance gaps amongst features, the developed FGSopt method improves the object detection performance of the FGS technique also improving upon several state-of-the-art salient object detection models. 4. The introduction of multiple combination schemes/rules further extends the generalisability of the traditional attention model beyond that of joint optimisation based single rules. The introduction of feature composition based grouping of images, enables the developed IGA method to autonomously identify an appropriate combination strategy for an unseen image. The results of a pair-wise ranksum test confirm that the IGA method is significantly better than the deterministic and classification based benchmark methods on the 99% confidence interval level. Extending this line of research, a novel relative encoding approach enables the adapted XCSCA method to group images having similar saliency prediction ability. By keeping track of previous inputs, the introduced action part of the XCSCA approach enables learning of generalised feature importance rules. By more accurate grouping of images as compared with IGA, generalised learnt rules and appropriate application of feature importance rules, the XCSCA approach improves upon the generalisation performance of the IGA method. 5. The introduced uniform saliency assignment and segmentation quality cues enable label free evaluation of a feature/saliency map. By accurate ranking and effective clustering, the developed DFS method successfully solves the complex problem of finding appropriate features for combination (on an-image-by-image basis) for the first time in saliency detection. The DFS method enables ground truth free evaluation of saliency methods and advances the state-of-the-art in data driven saliency aggregation by detection and deselection of redundant information. The final contribution is that the developed methods are formed into a complete system where analysis shows the effects of their interactions on the system. Based on the saliency prediction accuracy versus computational time trade-off, specialised variants of the proposed methods are presented along with the recommendations for further use by other saliency detection systems. This research work has shown that artificial learning can increase the generalisation of the traditional model of attention by effective selection and optimal combination of features. Overall, this thesis has shown that it is the ability to autonomously segregate images based on their types and subsequent learning of appropriate combinations that aid generalisation on difficult unseen stimuli.</p>

show abstract

Improving XCS performance on overlapping binary problems

Cited by 8 publications

References 11 publications

Comparison of two methods for computing action values in XCS with code-fragment actions

Comparison of two methods for computing action values in XCS with code-fragment actions

Learning Feature Selection and Combination Strategies for Generic Salient Object Detection

Learning Feature Selection and Combination Strategies for Generic Salient Object Detection

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