The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Teleantagonism: A pharmacodynamic property of the primary nociceptive neuron

Previous work from our group showed that intrathecal (i.t.) administration of substances such as glutamate, NMDA, or PGE(2) induced sensitization of the primary nociceptive neuron (PNN hypernociception) that was inhibited by a distal intraplantar (i.pl.) injection of either morphine or dipyrone. This pharmacodynamic phenomenon is referred to in the present work as ""teleantagonism``. We previously observed that the antinociceptive effect of i.t. morphine could be blocked by injecting inhibitors of the NO signaling pathway in the paw (i.pl.), and this effect was used to explain the mechanism of opioid-induced peripheral analgesia by i.t. administration. The objective of the present investigation was to determine whether this teleantagonism phenomenon was specific to this biochemical pathway (NO) or was a general property of the PNNs. Teleantagonism was investigated by administering test substances to the two ends of the PNN (i.e., to distal and proximal terminals; i.pl. plus i.t. or i.t. plus i.pl. injections). We found teleantagonism when: (i) inhibitors of the NO signaling pathway were injected distally during the antinociception induced by opioid agonists; (ii) a nonselective COX inhibitor was tested against PNN sensitization by IL-1 beta; (iii) selective opioid-receptor antagonists tested against antinociception induced by corresponding selective agonists. Although the dorsal root ganglion seems to be an important site for drug interactions, the teleantagonism phenomenon suggests that, in PNNs, a local sensitization spreads to the entire cell and constitutes an intriguing and not yet completely understood pharmacodynamic property of this group of neurons.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.