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.

Options for monitoring diabetic cats

The key to successful long-term management of diabetes in cats is individualization of advice to suit cat and owner. A relationship based on trust and cooperation between veterinarian and client leads invariably to the most satisfactory outcome. Success requires knowledge of the options for monitoring diabetic cats, selection and adaptation of appropriate techniques for each individual case, and provision of ongoing support and guidance for owners. The ongoing treatment of a diabetic cat can be one of the more rewarding experiences of feline practice, and many diabetic cats and their owners come to occupy a special place within the clinic environment. This chapter provides a comprehensive overview of the current options for monitoring diabetes in cats and guidelines for application of the techniques to clinical cases.

The Environment and the Selection of Aquaculture Species and Systems: An Economic Analysis

Environmental conditions play a significant role in the economic success of aquaculture. This article classifies environmental factors in a way that facilitates economic analysis of their implications for the selection of aquaculture species and systems. The implication of on-farm as on-site environmental conditions for this selection are considered first using profit-possibility frontiers and taking into account the biological law of environmental tolerance. However, in selecting, recommending and developing aquaculture species and systems, it is often unrealistic to assume the degree of managerial efficiency implied by the profit-possibility function. It is appropriate to take account of the degree of managerial inefficiency that actually exists, not all of which may be capable of being eliminated. Furthermore, experimental R&D should be geared to on-farm conditions, and the variability of these conditions needs to be taken into account. Particularly in shared water bodies, environmental spillovers between aquaculturalists can be important and as shown theoretically, can influence the socially optimal selection of aquaculture species and systems. Similarly, aquaculture can have environmental consequences for the rest of the community. The social economic implications of this for the selection of aquaculture species and systems are analyzed. Some paradoxical results are obtained. For example, if the quality of social governance of aquaculture is poor, aquaculture species and systems that cause a slow rate of environmental deterioration may be socially less satisfactory than those that cause a rapid rate of such deterioration. Socially optimal choice of aquaculture species and systems depends not only on their biophysical characteristics and market conditions but also on the prevailing state of governance of aquaculture. Failure to consider the last aspect can result in the introduction of new aquaculture species (and systems) doing more social harm than good.

Natural selection and quantitative genetics of life-history traits in Western women: A twin study

Whether contemporary human populations are still evolving as a result of natural selection has been hotly debated. For natural selection to cause evolutionary change in a trait, variation in the trait must be correlated with fitness and be genetically heritable and there must be no genetic constraints to evolution. These conditions have rarely been tested in human populations. In this study, data from a large twin cohort were used to assess whether selection Will cause a change among women in contemporary Western population for three life-history traits: age at menarche, age at first reproduction, and age at menopause. We control for temporal variation in fecundity (the baby boom phenomenon) and differences between women in educational background and religious affiliation. University-educated women have 35% lower fitness than those with less than seven years education, and Roman Catholic women have about 20% higher fitness than those of other religions. Although these differences were significant, education and religion only accounted for 2% and 1% of variance in fitness, respectively. Using structural equation modeling, we reveal significant genetic influences for all three life-history traits, with heritability estimates of 0.50, 0.23, and 0.45, respectively. However, strong genetic covariation with reproductive fitness could only be demonstrated for age at first reproduction, with much weaker covariation for age at menopause and no significant covariation for age at menarche. Selection may, therefore, lead to the evolution of earlier age at first reproduction in this population. We also estimate substantial heritable variation in fitness itself, with approximately 39% of the variance attributable to additive genetic effects, the remainder consisting of unique environmental effects and small effects from education and religion. We discuss mechanisms that could be maintaining such a high heritability for fitness. Most likely is that selection is now acting on different traits from which it did in pre-industrial human populations.

Matching prescription claims with medication data for nursing home residents: Implications for prescriber feedback, drug utilisation studies and selection of prescription claims database

Medication data retrieved from Australian Repatriation Pharmaceutical Benefits Scheme (RPBS) claims for 44 veterans residing in nursing homes and Pharmaceutical Benefits Scheme (PBS) claims for 898 nursing home residents were compared with medication data from nursing home records to determine the optimal time interval for retrieving claims data and its validity. Optimal matching was achieved using 12 weeks of RPBS claims data, with 60% of medications in the RPBS claims located in nursing home administration records, and 78% of medications administered to nursing home residents identified in RPBS claims. In comparison, 48% of medications administered to nursing home residents could be found in 12 weeks of PBS data, and 56% of medications present in PBS claims could be matched with nursing home administration records. RPBS claims data was superior to PBS, due to the larger number of scheduled items available to veterans and the veteran's file number, which acts as a unique identifier. These findings should be taken into account when using prescription claims data for medication histories, prescriber feedback, drug utilisation, intervention or epidemiological studies. (C) 2001 Elsevier Science Inc. All rights reserved.