An efficient algorithm for the incremental construction of a piecewise linear classifier
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
- Date: 2011
- Type: Text , Journal article
- Relation: Information Systems Vol. 36, no. 4 (2011), p. 782-790
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Reviewed:
- Description: In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called "indeterminate" regions between sets. In the second step sets are separated inside these "indeterminate" regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers. © 2010 Elsevier B.V. All rights reserved.
Fast modified global k-means algorithm for incremental cluster construction
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
- Date: 2011
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 44, no. 4 (2011), p. 866-876
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Reviewed:
- Description: The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the modified global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Numerical experiments show that these algorithms considerably improve the k-means algorithm. However, they require storing the whole affinity matrix or computing this matrix at each iteration. This makes both algorithms time consuming and memory demanding for clustering even moderately large datasets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points lying in different parts of the dataset. We exploit information gathered in previous iterations of the incremental algorithm to eliminate the need of computing or storing the whole affinity matrix and thereby to reduce computational effort and memory usage. Results of numerical experiments on six standard datasets demonstrate that the new algorithm is more efficient than the global and the modified global k-means algorithms. © 2010 Elsevier Ltd. All rights reserved.
A novel piecewise linear classifier based on polyhedral conic and max-min separabilities
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean , Ozturk, Gurkan , Kasimbeyli, Refail
- Date: 2011
- Type: Text , Journal article
- Relation: TOP Vol.21, no.1 (2011), p. 1-22
- Full Text: false
- Reviewed:
- Description: In this paper, an algorithm for finding piecewise linear boundaries between pattern classes is developed. This algorithm consists of two main stages. In the first stage, a polyhedral conic set is used to identify data points which lie inside their classes, and in the second stage we exclude those points to compute a piecewise linear boundary using the remaining data points. Piecewise linear boundaries are computed incrementally starting with one hyperplane. Such an approach allows one to significantly reduce the computational effort in many large data sets. Results of numerical experiments are reported. These results demonstrate that the new algorithm consistently produces a good test set accuracy on most data sets comparing with a number of other mainstream classifiers. © 2011 Sociedad de EstadÃstica e Investigación Operativa.
Compiling and using input-output frameworks through collaborative virtual laboratories
- Authors: Lenzen, Manfred , Geschke, Arne , Wiedmann, Thomas , Lane, Joe , Anderson, Neal , Baynes, Timothy , Boland, John , Daniels, Peter , Dey, Christopher , Fry, Jacob , Hadjikakou, Michalis , Kenway, Steven , Malik, Arunima , Moran, Daniel , Murray, Joy , Nettleton, Stuart , Poruschi, Lavinia , Reynolds, Christian , Rowley, Hazel , Ugon, Julien , Webb, Dean , West, James
- Date: 2014
- Type: Text , Journal article
- Relation: Science of the Total Environment Vol. 485-486, no. 1 (July 2014), p. 241-251
- Full Text: false
- Reviewed:
- Description: Compiling, deploying and utilising large-scale databases that integrate environmental and economic data have traditionally been labour- and cost-intensive processes, hindered by the large amount of disparate and misaligned data that must be collected and harmonised. The Australian Industrial Ecology Virtual Laboratory (IELab) is a novel, collaborative approach to compiling large-scale environmentally extended multi-region input-output (MRIO) models.The utility of the IELab product is greatly enhanced by avoiding the need to lock in an MRIO structure at the time the MRIO system is developed. The IELab advances the idea of the "mother-daughter" construction principle, whereby a regionally and sectorally very detailed "mother" table is set up, from which "daughter" tables are derived to suit specific research questions. By introducing a third tier - the "root classification" - IELab users are able to define their own mother-MRIO configuration, at no additional cost in terms of data handling. Customised mother-MRIOs can then be built, which maximise disaggregation in aspects that are useful to a family of research questions.The second innovation in the IELab system is to provide a highly automated collaborative research platform in a cloud-computing environment, greatly expediting workflows and making these computational benefits accessible to all users.Combining these two aspects realises many benefits. The collaborative nature of the IELab development project allows significant savings in resources. Timely deployment is possible by coupling automation procedures with the comprehensive input from multiple teams. User-defined MRIO tables, coupled with high performance computing, mean that MRIO analysis will be useful and accessible for a great many more research applications than would otherwise be possible. By ensuring that a common set of analytical tools such as for hybrid life-cycle assessment is adopted, the IELab will facilitate the harmonisation of fragmented, dispersed and misaligned raw data for the benefit of all interested parties.
Classification through incremental max-min separability
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean , Karasozen, Bulent
- Date: 2011
- Type: Text , Journal article
- Relation: Pattern Analysis and Applications Vol. 14, no. 2 (2011), p. 165-174
- Relation: http://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Reviewed:
- Description: Piecewise linear functions can be used to approximate non-linear decision boundaries between pattern classes. Piecewise linear boundaries are known to provide efficient real-time classifiers. However, they require a long training time. Finding piecewise linear boundaries between sets is a difficult optimization problem. Most approaches use heuristics to avoid solving this problem, which may lead to suboptimal piecewise linear boundaries. In this paper, we propose an algorithm for globally training hyperplanes using an incremental approach. Such an approach allows one to find a near global minimizer of the classification error function and to compute as few hyperplanes as needed for separating sets. We apply this algorithm for solving supervised data classification problems and report the results of numerical experiments on real-world data sets. These results demonstrate that the new algorithm requires a reasonable training time and its test set accuracy is consistently good on most data sets compared with mainstream classifiers. © 2010 Springer-Verlag London Limited.
A new modified global k-means algorithm for clustering large data sets
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at XIIIth International Conference : Applied Stochastic Models and Data Analysis, ASMDA 2009, Vilnius, Lithuania : 30th June - 3rd July 2009 p. 1-5
- Full Text: false
- Description: The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, in order to resolve difficulties with the choice of starting points, incremental approaches have been developed. The modified global k-means algorithm is based on such an approach. It iteratively adds one cluster center at a time. Numerical experiments show that this algorithm considerably improve the k-means algorithm. However, this algorithm is not suitable for clustering very large data sets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points spanning different parts of the data set. We exploit information gathered in previous iterations of the incremental algorithm to reduce its complexity.
- Description: 2003007558
An incremental approach for the construction of a piecewise linear classifier
- Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at XIIIth International Conference : Applied Stochastic Models and Data Analysis, ASMDA 2009, Vilnius, Lithuania : 30th June - 3rd July 2009 p. 507–511
- Relation: https://purl.org/au-research/grants/arc/DP0666061
- Full Text: false
- Description: In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called “indeterminate” regions between sets. In the second step sets are separated inside these “indeterminate” regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers.
- Description: 2003007559