Home Dissolution: What Happens After Separation?

Building a home and creating a family are highly interconnected processes. So what happens to the home when people separate or divorce? This paper addresses this question using both a quantitative and a qualitative approach. Based on an extensive database with socio-economic background data of 42 000 separated Danish couples in 2002, the housing careers of both partners were followed to see how the housing situation of different types of people was affected by the separation. In the qualitative approach, nine interviews with couples who had parted shed light on the emotions and practical problems of dissolving a home: how to decide who should stay? Who should move? What was it like to stay alone in, or to leave, the matrimonial home?     

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The Energy Concept Adviser—A tool to improve energy efficiency in educational buildings

Educational buildings such as kindergartens, schools and universities display many similar design, operation and maintenance features in most countries. The two most noteworthy similarities amongst these building types are the high energy consumption and the necessity for retrofitting many buildings within this sector. However, studies have shown that during retrofit, energy saving measures are only rarely applied, because the decision-makers lack knowledge of investments and the efficiency of potential energy saving measures. The main goal of the International Energy Agency ECBCS Annex 36 is to provide the educational building decision-makers with sufficient data, information and tools to improve their learning and teaching environments by improving the energy efficiency of their buildings.     

Pessimistic cost-sensitive active learning of decision trees for profit maximizing targeting campaigns

In business applications such as direct marketing, decision-makers are required to choose the action which best maximizes a utility function. Cost-sensitive learning methods can help them achieve this goal. In this paper, we introduce Pessimistic Active Learning (PAL). PAL employs a novel pessimistic measure, which relies on confidence intervals and is used to balance the exploration/exploitation trade-off. In order to acquire an initial sample of labeled data, PAL applies orthogonal arrays of fractional factorial design. PAL was tested on ten datasets using a decision tree inducer. A comparison of these results to those of other methods indicates PAL’s superiority.