title: On dynamic feature weighting for feature drifting data streams

authors: Barddal, Jean Paul

subjects: computer science

abstract: The ubiquity of data streams has been encouraging the development of new incremental and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but mainly, tailored to adapt to possible changes in the data distribution, a phenomenon named concept drift. Recently, several works have shown the impact of a so far nearly neglected type of drifcccct: feature drifts. Feature drifts occur whenever a subset of features becomes, or ceases to be, relevant to the learning task. In this paper we (i) provide insights into how the relevance of features can be tracked as a stream progresses according to information theoretical Symmetrical Uncertainty; and (ii) how it can be used to boost two learning schemes: Naive Bayesian and k-Nearest Neighbor. Furthermore, we investigate the usage of these two new dynamically weighted learners as prediction models in the leaves of the Hoeffding Adaptive Tree classifier. Results show improvements in accuracy (an average of 10.69% for k-Nearest Neighbor, 6.23% for Naive Bayes and 4.42% for Hoeffding Adaptive Trees) in both synthetic and real-world datasets at the expense of a bounded increase in both memory consumption and processing time.

text: On dynamic feature weighting for feature drifting data streams The ubiquity of data streams has been encouraging the development of new incremental and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but mainly, tailored to adapt to possible changes in the data distribution, a phenomenon named concept drift. Recently, several works have shown the impact of a so far nearly neglected type of drifcccct: feature drifts. Feature drifts occur whenever a subset of features becomes, or ceases to be, relevant to the learning task. In this paper we (i) provide insights into how the relevance of features can be tracked as a stream progresses according to information theoretical Symmetrical Uncertainty; and (ii) how it can be used to boost two learning schemes: Naive Bayesian and k-Nearest Neighbor. Furthermore, we investigate the usage of these two new dynamically weighted learners as prediction models in the leaves of the Hoeffding Adaptive Tree classifier. Results show improvements in accuracy (an average of 10.69% for k-Nearest Neighbor, 6.23% for Naive Bayes and 4.42% for Hoeffding Adaptive Trees) in both synthetic and real-world datasets at the expense of a bounded increase in both memory consumption and processing time.

year: 2016

title: On the first G₁ stiff fluid spike solution in General Relativity

authors: Coley, A.A.

subjects: Science & Technology

abstract: Using the Geroch transformation we obtain the first example of an exact stiff fluid spike solution to the Einstein field equations in a closed form exhibiting a spacelike G₁ group of symmetries (i.e., with a single isometry). This new solution is of Petrov type I and exhibits a spike crossing which persists to the past, which allows us to better understand spike crossings in the context of structure formation.

text: On the first G₁ stiff fluid spike solution in General Relativity Using the Geroch transformation we obtain the first example of an exact stiff fluid spike solution to the Einstein field equations in a closed form exhibiting a spacelike G₁ group of symmetries (i.e., with a single isometry). This new solution is of Petrov type I and exhibits a spike crossing which persists to the past, which allows us to better understand spike crossings in the context of structure formation.

year: 2016

title: Maori perspectives of the environment: A review of Environment Waikato information sources.

authors: Whangapirita, Laura

subjects: Maori

abstract: The Community and Economy Programme at Environment Waikato has developed a partnership with the Maori and Psychology Research Unit of the University of Waikato to further assist in strengthening its commitment to cultural research and information work. This relationship will facilitate the development of a number of significant research reports throughout 2003.  This initial report describes an internal review conducted by the Maori and Psychology Research Unit to identify what information Environment Waikato currently holds on Maori environmental perspectives.

text: Maori perspectives of the environment: A review of Environment Waikato information sources. The Community and Economy Programme at Environment Waikato has developed a partnership with the Maori and Psychology Research Unit of the University of Waikato to further assist in strengthening its commitment to cultural research and information work. This relationship will facilitate the development of a number of significant research reports throughout 2003.  This initial report describes an internal review conducted by the Maori and Psychology Research Unit to identify what information Environment Waikato currently holds on Maori environmental perspectives.

year: 2003-05