Information-Based Evaluation Criterion for Classifier's Performance

In the past few years many systems for learning decision rules from examples were developed. As different systems allow different types of answers when classifying new instances, it is difficult to appropriately evaluate the systems' classification power in comparison with other classification systems or in comparison with human experts. Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier's answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.     

ILP turns 20

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.     

Age differences in personality across the adult life span: Parallels in five cultures.

Both cross-sectional and longitudinal studies in the United States have shown consistent changes between college age and middle adulthood. There appear to be declines in 3 of the 5 major factors of personality-Neuroticism, Extraversion, and Openness-and increases in Agreeableness and Conscientiousness. To examine cross-cultural generalizability of these findings, translations of the Revised NEO Personality Inventory were administered to samples in Germany, Italy, Portugal, Croatia, and South Korea (N?=?7,363). Similar patterns of age differences were seen in each country, for both men and women. Common trends were also seen for the more specific traits that define the major factors. Because these nations differ substantially in culture and recent history, results suggest the hypothesis that these are universal maturational changes in adult personality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Q 2 LEARNING AND ITS APPLICATION TO CAR MODELLING

In this paper we describe an application of Q ² learning, a recently developed approach to machine learning in numerical domains (?uc et al., 2003 ?uc , D. , D. Vladu?i? , and I. Bratko . 2003 . Qualitatively faithful quantitative prediction . Proceedings of the eighteenth International Joint Conference on Artificial Intelligence , 1052 – 1057 , Acapulco . Morgan Kaufmann . [Google Scholar] 2004 ?uc , D. , D. Vladu?i? , and I. Bratko . 2004 . Qualitatively faithful quantitative prediction . Artificial Intelligence , 158 : 189 – 214 . [CSA] [CROSSREF]  [Google Scholar]) to the automated modelling of a complex, industrially relevant mechanical system – a four wheel suspension and steering system of a car. In this experiment, first a qualitative model of this dynamic system was induced from data, and then this model was reified into a quantitative model. The induced qualitative models enable explanation of relations among the variables in the system and, when reified into quantitative models, enable accurate numerical prediction. Furthermore, the qualitative guidance of the quantitative modelling process leads to predictions that are significantly more accurate than those obtained by state-of-the-art numerical learning methods.