The Problem of Maps

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Practical feature subset selection for machine learning

Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithm

Feature subset selection: a correlation based filter approach

Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or redundant to the learning task. Feature subset selection, then, is a method of enhancing the performance of learning algorithms, reducing the hypothesis search space, and, in some cases, reducing the storage requirement. This paper describes a feature subset selector that uses a correlation based heuristic to determine the goodness of feature subsets, and evaluates its effectiveness with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based learner(IBI). Experiments using a number of standard data sets drawn from real and artificial domains are presented. Feature subset selection gave significant improvement for all three algorithms; C4.5 generated smaller decision trees

Price Leadership in UK Food Retailing: Time Series Representation and Evidence

This paper analyses the price of a common basket of products sold in each of the UK’s four largest retail chains to assess propositions regarding price leadership. Data used in this investigation represent weighted average prices of a large group of branded and non-branded products purchased nationally at weekly intervals over a three and half year period and cover purchases in 37 product categories. The data are analysed using vector autoregressive methods, a convenient framework for a statistical investigation of this sort, owing to the time series properties that the price data exhibit. The paper introduces the concepts of strategic and tactical price leadership. Since these correspond to parameter restrictions in the vector autoregression, the statistical tests have a economically meaningful interpretation. While the empirical findings are preliminary, they indicate that Tesco, the largest of the retail chains, acts as price leader in both the strategic and tactical senses.

Impact of the Delta (1232) resonance on neutral pion photoproduction in chiral perturbation theory

We present an ongoing project to assess the importance of D-waves and the $\Delta (1232)$ resonance for descriptions of neutral pion photoproduction in Heavy Baryon Chiral Perturbation Theory. This research has been motivated by data published by the A2 and CB-TAPS collaborations at MAMI [1]. This data has reached unprecedented levels of accuracy from threshold through to the $\Delta$ resonance. Accompanying the experimental work, there has also been a series of publications studying the theory that show that, to go beyond an energy of $E_\gamma=170$ MeV, it is necessary to include other aspects, in particular the $\Delta (1232)$ as a degree of freedom [2] and possibly higher partial waves [3].Comment: Proceedings to the 8th International Workshop on Chiral Dynamics 201

The Kink Phenomenon in Fejér and Clenshaw-Curtis Quadrature

The Fejér and Clenshaw-Curtis rules for numerical integration exhibit a curious phenomenon when applied to certain analytic functions. When N, (the number of points in the integration rule) increases, the error does not decay to zero evenly but does so in two distinct stages. For N less than a critical value, the error behaves like $O(\varrho^{-2N})$, where $\varrho$ is a constant greater than 1. For these values of N the accuracy of both the Fejér and Clenshaw-Curtis rules is almost indistinguishable from that of the more celebrated Gauss-Legendre quadrature rule. For larger N, however, the error decreases at the rate $O(\varrho^{-N})$, i.e., only half as fast as before. Convergence curves typically display a kink where the convergence rate cuts in half. In this paper we derive explicit as well as asymptotic error formulas that provide a complete description of this phenomenon.\ud \ud This work was supported by the Royal Society of the UK and the National Research Foundation of South Africa under the South Africa-UK Science Network Scheme. The first author also acknowledges grant FA2005032300018 of the NRF

PRICE LINKAGES IN THE INTERNATIONAL WHEAT MARKET

This paper brings time series techniques to bear on the relationships between the prices of the principal types of wheat traded internationally. In all, the relationships between eleven wheat prices (categorised by wheat quality, harvest date and port of despatch) are scrutinised to uncover the structure of the wheat market implicit in the behaviour its prices reveal. The statistical evidence supports the notion of a highly integrated market that is segmented according to wheat strength-the principal determinant of end-use. Three segments are identified: a market for 'strong' (bread-making) wheat, another for 'weak' (confectionary products-making) wheat and a third for medium strength wheat suitable for unleavened breads and noodles. Whilst informative, market integration - detected by cointegration among prices - is not altogether surprising, yet the presence of cointegration implies a causal structure, which is of more cogent interest. Among a number of complementary techniques, linkages are uncovered using an innovative concept of irreducible cointegration vectors (Davidson 1998, Barassi et al 2001) which provides new evidence on price linkages. Statistical evidence is robust and not test-dependent. Specifically, we find a dominant price leader in each sub-market. In terms of its pricing, the EU is found to play a passive role in the world market, confirming a widely held view.Wheat market, price linkages, irreducible cointegration vectors, Crop Production/Industries, International Relations/Trade,

Effect of Hot Baryons on the Weak-Lensing Shear Power Spectrum

We investigate the impact of the intracluster medium on the weak-lensing shear power spectrum (PS). Using a halo model we find that, compared to the dark matter only case, baryonic pressure leads to a suppression of the shear PS on the order of a few percent or more for $l \gtrsim 1000$. Cooling/cooled baryons and the intergalactic medium can further alter the shear PS. Therefore, the interpretation of future precision weak lensing data at high multipoles must take into account the effects of baryons.Comment: 4 pages, 3 figure