title: Towards Meta-learning over Data Streams

authors: van Rijn, Jan N.

subjects: Machine learning

abstract: Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform particularly well on which kinds of data. Moreover, it is possible that the characteristics of the data change over time, and thus that a different algorithm should be recommended at various points in time. Figure 1 illustrates this. As such, we are dealing with the Algorithm Selection Problem [9] in a data stream setting. Based on measurable meta-features from a window of observations from a data stream, a meta-algorithm is built that predicts the best classifier for the next window. Our results show that this meta-algorithm is competitive with state-of-the art data streaming ensembles, such as OzaBag [6], OzaBoost [6] and Leveraged Bagging [3].

text: Towards Meta-learning over Data Streams Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform particularly well on which kinds of data. Moreover, it is possible that the characteristics of the data change over time, and thus that a different algorithm should be recommended at various points in time. Figure 1 illustrates this. As such, we are dealing with the Algorithm Selection Problem [9] in a data stream setting. Based on measurable meta-features from a window of observations from a data stream, a meta-algorithm is built that predicts the best classifier for the next window. Our results show that this meta-algorithm is competitive with state-of-the art data streaming ensembles, such as OzaBag [6], OzaBoost [6] and Leveraged Bagging [3].

year: 2014

title: Efficient algorithms for conditional independence inference

authors: Bouckaert, Remco R.

subjects: computer science

abstract: The topic of the paper is computer testing of (probabilistic) conditional independence (CI) implications by an algebraic method of structural imsets. The basic idea is to transform (sets of) CI statements into certain integral vectors and to verify by a computer the corresponding algebraic relation between the vectors, called the independence implication. We interpret the previous methods for computer testing of this implication from the point of view of polyhedral geometry. However, the main contribution of the paper is a new method, based on linear programming (LP). The new method overcomes the limitation of former methods to the number of involved variables. We recall/describe the theoretical basis for all four methods involved in our computational experiments, whose aim was to compare the efficiency of the algorithms. The experiments show that the LP method is clearly the fastest one. As an example of possible application of such algorithms we show that testing inclusion of Bayesian network structures or whether a CI statement is encoded in an acyclic directed graph can be done by the algebraic method.

text: Efficient algorithms for conditional independence inference The topic of the paper is computer testing of (probabilistic) conditional independence (CI) implications by an algebraic method of structural imsets. The basic idea is to transform (sets of) CI statements into certain integral vectors and to verify by a computer the corresponding algebraic relation between the vectors, called the independence implication. We interpret the previous methods for computer testing of this implication from the point of view of polyhedral geometry. However, the main contribution of the paper is a new method, based on linear programming (LP). The new method overcomes the limitation of former methods to the number of involved variables. We recall/describe the theoretical basis for all four methods involved in our computational experiments, whose aim was to compare the efficiency of the algorithms. The experiments show that the LP method is clearly the fastest one. As an example of possible application of such algorithms we show that testing inclusion of Bayesian network structures or whether a CI statement is encoded in an acyclic directed graph can be done by the algebraic method.

year: 2010

title: Opening Address to the National Māori Graduates of Psychology Symposium 2002: Making a difference

authors: Nikora, Linda Waimarie

subjects: Maori

abstract: The opening address to the National Māori Graduates of Psychology Symposium 2002.  This address introduces the themes of the conference: Kia matāra - negotiating the challenges in Māori development, kia mau – recruitment and retention, Tuhia mai, whiua atu – research and methodology, tinia mai – intervantions and treatment, taitaia i te ahi manuka – pride upon the skin.

text: Opening Address to the National Māori Graduates of Psychology Symposium 2002: Making a difference The opening address to the National Māori Graduates of Psychology Symposium 2002.  This address introduces the themes of the conference: Kia matāra - negotiating the challenges in Māori development, kia mau – recruitment and retention, Tuhia mai, whiua atu – research and methodology, tinia mai – intervantions and treatment, taitaia i te ahi manuka – pride upon the skin.

year: 2003