Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file

The Association between Fatal Coronary Heart Disease and Ambient Particulate Air Pollution: Are Females at Greater Risk?

The purpose of this study was to assess the effect of long-term ambient particulate matter (PM) on risk of fatal coronary heart disease (CHD). A cohort of 3,239 nonsmoking, non-Hispanic white adults was followed for 22 years. Monthly concentrations of ambient air pollutants were obtained from monitoring stations [PM < 10 μm in aerodynamic diameter (PM(10)), ozone, sulfur dioxide, nitrogen dioxide] or airport visibility data [PM < 2.5 μm in aerodynamic diameter (PM(2.5))] and interpolated to ZIP code centroids of work and residence locations. All participants had completed a detailed lifestyle questionnaire at baseline (1976), and follow-up information on environmental tobacco smoke and other personal sources of air pollution were available from four subsequent questionnaires from 1977 through 2000. Persons with prevalent CHD, stroke, or diabetes at baseline (1976) were excluded, and analyses were controlled for a number of potential confounders, including lifestyle. In females, the relative risk (RR) for fatal CHD with each 10-μg/m(3) increase in PM(2.5) was 1.42 [95% confidence interval (CI), 1.06–1.90] in the single-pollutant model and 2.00 (95% CI, 1.51–2.64) in the two-pollutant model with O(3). Corresponding RRs for a 10-μg/m(3) increase in PM(10-2.5) and PM(10) were 1.62 and 1.45, respectively, in all females and 1.85 and 1.52 in postmenopausal females. No associations were found in males. A positive association with fatal CHD was found with all three PM fractions in females but not in males. The risk estimates were strengthened when adjusting for gaseous pollutants, especially O(3), and were highest for PM(2.5). These findings could have great implications for policy regulations

Selecting and Ranking Time Series Models Using the NOEMON Approach

Abstract. In this work, we proposed to use the NOEMON approach to rank and select time series models. Given a time series, the NOEMON approach provides a ranking of the candidate models to forecast that series, by combining the outputs of different learners. The best ranked models are then returned as the selected ones. In order to evaluate the proposed solution, we implemented a prototype that used MLP neural networks as the learners. Our experiments using this prototype revealed encouraging results.

Applying machine learning techniques to DNA sequence analysis

We are developing a machine learning system that modifies existing knowledge about specific types of biological sequences. It does this by considering sample members and nonmembers of the sequence motif being learned. Using this information (which we call a domain theory''), our learning algorithm produces a more accurate representation of the knowledge needed to categorize future sequences. Specifically, the KBANN algorithm maps inference rules, such as consensus sequences, into a neural (connectionist) network. Neural network training techniques then use the training examples of refine these inference rules. We have been applying this approach to several problems in DNA sequence analysis and have also been extending the capabilities of our learning system along several dimensions