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Clustering by a genetic algorithm with biased mutation operator
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. (Anders Lansner)
2010 (English)In: 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2010, 1-8 p.Conference paper (Refereed)
Abstract [en]

In this paper we propose a genetic al- gorithm that partitions data into a given number of clusters. The algorithm can use any cluster validity function as fitness function. Cluster validity is used as a criterion for cross-over operations. The cluster assignment for each point is accompanied by a tem- perature and points with low confidence are pref- erentially mutated. We present results applying this genetic algorithm to several UCI machine learning data sets and using several objective cluster validity functions for optimization. It is shown that given an appropriate criterion function, the algorithm is able to converge on good cluster partitions within few generations. Our main contributions are: 1. to present a genetic algorithm that is fast and able to converge on meaningful clusters for real-world data sets, 2. to define and compare several cluster validity criteria. 

Place, publisher, year, edition, pages
IEEE , 2010. 1-8 p.
Keyword [en]
learning (artificial intelligence), pattern clustering, UCI machine learning, cluster validity function, criterion function, crossover operation, fitness function, genetic algorithm, mutation operator, optimization
National Category
Signal Processing
Research subject
URN: urn:nbn:se:kth:diva-48065DOI: 10.1109/CEC.2010.5586090ISI: 000287375801062OAI: diva2:456742
2010 IEEE World Congress on Computational Intelligence. Barcelona, SPAIN. JUL 18-23, 2010
© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20111115Available from: 2012-02-20 Created: 2011-11-15 Last updated: 2012-03-12Bibliographically approved
In thesis
1. Machine Learning Techniques with Specific Application to the Early Olfactory System
Open this publication in new window or tab >>Machine Learning Techniques with Specific Application to the Early Olfactory System
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below.

We have performed a systematic investigation for classification in biomedical images with the goal of recognizing a material in these images by its texture. This investigation included (i) different measures for evaluating the importance of image descriptors (features), (ii) methods to select a feature set based on these evaluations, and (iii) classification algorithms. Image features were evaluated according to their estimated relevance for the classification task and their redundancy with other features. For this purpose, we proposed a framework for relevance and redundancy measures and, within this framework, we proposed two new measures. These were the value difference metric and the fit criterion. Both measures performed well in comparison with other previously used ones for evaluating features. We also proposed a Hopfield network as a method for feature selection, which in experiments gave one of the best results relative to other previously used approaches.

We proposed a genetic algorithm for clustering and tested it on several realworld datasets. This genetic algorithm was novel in several ways, including (i) the use of intra-cluster distance as additional optimization criterion, (ii) an annealing procedure, and (iii) adaptation of mutation rates. As opposed to many conventional clustering algorithms, our optimization framework allowed us to use different cluster validation measures including those which do not rely on cluster centroids. We demonstrated the use of the clustering algorithm experimentally with several cluster validity measures as optimization criteria. We compared the performance of our clustering algorithm to that of the often-used fuzzy c-means algorithm on several standard machine learning datasets from the University of California/Urvine (UCI) and obtained good results.

The organization of representations in the brain has been observed at several stages of processing to spatially decompose input from the environment into features that are somehow relevant from a behavioral or perceptual standpoint. For the perception of smells, the analysis of such an organization, however, is not as straightforward because of the missing metric. Some studies report spatial clusters for several combinations of physico-chemical properties in the olfactory bulb at the level of the glomeruli. We performed a systematic study of representations based on a dataset of activity-related images comprising more than 350 odorants and covering the whole spatial array of the first synaptic level in the olfactory system. We found clustered representations for several physico-chemical properties. We compared the relevance of these properties to activations and estimated the size of the coding zones. The results confirmed and extended previous studies on olfactory coding for physico-chemical properties. Particularly of interest was the spatial progression by carbon chain that we found. We discussed our estimates of relevance and coding size in the context of processing strategies. We think that the results obtained in this study could guide the search into olfactory coding primitives and the understanding of the stimulus space.

In a second study on representations in the olfactory bulb, we grouped odorants together by perceptual categories, such as floral and fruity. By the application of the same statistical methods as in the previous study, we found clustered zones for these categories. Furthermore, we found that distances between spatial representations were related to perceptual differences in humans as reported in the literature. This was possibly the first time that such an analysis had been done. Apart from pointing towards a spatial decomposition by perceptual dimensions, results indicate that distance relationships between representations could be perceptually meaningful.

In a third study, we modeled axon convergence from olfactory receptor neurons to the olfactory bulb. Sensory neurons were stimulated by a set of biologically-relevant odors, which were described by a set of physico-chemical properties that covaried with the neural and glomerular population activity in the olfactory bulb. Convergence was mediated by the covariance between olfactory neurons. In our model, we could replicate the formation of glomeruli and concentration coding as reported in the literature, and further, we found that the spatial relationships between representational zones resulting from our model correlated with reported perceptual differences between odor categories. This shows that natural statistics, including similarity of physico-chemical structure of odorants, can give rise to an ordered arrangement of representations at the olfactory bulb level where the distances between representations are perceptually relevant.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xiv, 216 p.
Trita-CSC-A, ISSN 1653-5723 ; 2012:01
feature selection, image features, pattern classification, relevance, redundancy, distributional similarity, divergence measure, genetic algorithms, clustering algorithms, annealing, olfactory coding, olfactory bulb, odorants, glomeruli, property-activity relationship, olfaction, plasticity, axonal guidance, odor category, perception, spatial coding, population coding, memory organization, odor quality
National Category
Biological Sciences
Research subject
urn:nbn:se:kth:diva-90474 (URN)978-91-7501-273-5 (ISBN)
Public defence
2012-03-16, D3, Lindstedtsvägen 5, KTH, Stockholm, 10:00 (English)
Swedish e‐Science Research Center

QC 20120224

Available from: 2012-02-24 Created: 2012-02-24 Last updated: 2013-04-09Bibliographically approved

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