eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Understanding the dimensional change card sort: Perspectives from task success and failure

Four experiments consider some of the circumstances under which children follow two different rule pairs when sorting cards. Previous research has repeatedly found that 3-year-olds encounter substantial difficulties implementing the second of two conflicting rule sets, despite their knowledge of these rules. One interpretation of this phenomenon [Cognitive Complexity and Control (CCC) theory] is that 3-year-olds have problems establishing an appropriate hierarchical ordering for rules. The present data suggest an alternative account of children's card sorting behaviour, according to which the cognitive salience of test card features may be more important than inflexibility with respect to rule representation.

Do EU direct payments to beef producers belong in the 'blue box'?

In the Uruguay Round Agreement on Agriculture, so-called 'blue box' support measures were exempted from reduction commitments, provided they were delivered under 'production-limiting' programs. Although classified as 'blue box', the EU system of direct payments (DP) to beef farmers imposes 'claim-limiting' restrictions rather than 'production-limiting' restrictions, allowing farmers to keep additional animals over and above the number upon which they are eligible to claim DP. The present paper provides empirical evidence that EU direct payments capitalise into the market prices of male calves and young steers in Ireland. It is also likely that DP capitalises into the prices of beef cows and heifers. Given this capitalisation process, some farmers can obtain 'capitalised' DP on animals produced over and above the 'claim-limiting' restrictions, by selling these animals through auction markets. Thus, 'capitalised' DP probably encourages production over and above the limiting measures.

Selection of funding schemes by a borrowing decision model: a Hong Kong case study

In financial decision-making, a number of mathematical models have been developed for financial management in construction. However, optimizing both qualitative and quantitative factors and the semi-structured nature of construction finance optimization problems are key challenges in solving construction finance decisions. The selection of funding schemes by a modified construction loan acquisition model is solved by an adaptive genetic algorithm (AGA) approach. The basic objectives of the model are to optimize the loan and to minimize the interest payments for all projects. Multiple projects being undertaken by a medium-size construction firm in Hong Kong were used as a real case study to demonstrate the application of the model to the borrowing decision problems. A compromise monthly borrowing schedule was finally achieved. The results indicate that Small and Medium Enterprise (SME) Loan Guarantee Scheme (SGS) was first identified as the source of external financing. Selection of sources of funding can then be made to avoid the possibility of financial problems in the firm by classifying qualitative factors into external, interactive and internal types and taking additional qualitative factors including sovereignty, credit ability and networking into consideration. Thus a more accurate, objective and reliable borrowing decision can be provided for the decision-maker to analyse the financial options.

Credit flow [poetry]

A poem within the Alhambra Poetry Calendar 2011, a desk calendar and poetry anthology in one.