Showing papers on "Selection (genetic algorithm) published in 1998"
TL;DR: In this article, a semiparametric method is developed to estimate the bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify matching, selection models, and the method of difference-in-differences.
Abstract: Semiparametric methods are developed to estimate the bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify matching, selection models, and the method of difference-in-differences. Using data from an experiment on a prototypical social program and data from nonexperimental comparison groups, we reject the assumptions justifying matching and our extensions of it. The evidence supports the selection bias model and the assumptions that justify a semiparametric version of the method of difference-in-differences. We extend our analysis to consider applications of the methods to ordinary observational data.
1,929 citations
Posted Content•
TL;DR: Semiparametric econometric methods are applied to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators.
Abstract: This paper develops and applies semiparametric econometric methods to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators and our extensions of them: (a) the method of matching; (b) the classical econometric selection model which represents the bias solely as a function of the probability of participation; and (c) the method of difference-in-differences. Using data from an experiment on a prototypical social program combined with unusually rich data from a nonexperimental comparison group, we reject the assumptions justifying matching and our extensions of that method but find evidence in support of the index-sufficient selection bias model and the assumptions that justify application of a conditional semiparametric version of the method of difference-in-difference. Fa comparable people and to appropriately weight participants and nonparticipants a sources of selection bias as conveniently measured. We present a rigorous defin bias and find that in our data it is a small component of conventially meausred it is still substantial when compared with experimentally-estimated program impa matching participants to comparison group members in the same labor market, givi same questionnaire, and making sure they have comparable characteristics substan the performance of any econometric program evaluation estimator. We show how t analysis to estimate the impact of treatment on the treated using ordinary obser
1,575 citations
TL;DR: The authors' approach uses a genetic algorithm to select subsets of attributes or features to represent the patterns to be classified, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
Abstract: Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
1,465 citations
TL;DR: In this paper, a survey of the available methods for estimating models with sample selection bias is presented, including semi-parametric and fully parameterized models, and the ability to tackle different selection rules generating the selection bias.
Abstract: This paper surveys the available methods for estimating models with sample selection bias. I initially examine the fully parameterized model proposed by Heckman (1979) before investigating departures in two directions. First, I consider the relaxation of distributional assumptions. In doing so I present the available semi-parametric procedures. Second, I investigate the ability to tackle different selection rules generating the selection bias. Finally, I discuss how the estimation procedures applied in the cross-sectional case can be extended to panel data.
1,107 citations
01 Jan 1998
1,087 citations
TL;DR: A biological explanation for the occurrence of negative side effects of selection is presented and future application of modern reproduction and DNA-techniques in animal breeding may increase production levels even faster than at present, which may result in more dramatic consequences for behavioural, physiological and immunological traits.
960 citations
TL;DR: This book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems.
Abstract: From the Publisher:
The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference book for those who are conducting research about feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.
953 citations
Posted Content•
TL;DR: Semiparametric econometric methods are applied to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators.
Abstract: This paper develops and applies semiparametric econometric methods to estimate the form of selection bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify three widely-used classes of estimators and our extensions of them: (a) the method of matching; (b) the classical econometric selection model which represents the bias solely as a function of the probability of participation; and (c) the method of difference-in-differences. Using data from an experiment on a prototypical social program combined with unusually rich data from a nonexperimental comparison group, we reject the assumptions justifying matching and our extensions of that method but find evidence in support of the index-sufficient selection bias model and the assumptions that justify application of a conditional semiparametric version of the method of difference-in-difference. Fa comparable people and to appropriately weight participants and nonparticipants a sources of selection bias as conveniently measured. We present a rigorous defin bias and find that in our data it is a small component of conventially meausred it is still substantial when compared with experimentally-estimated program impa matching participants to comparison group members in the same labor market, givi same questionnaire, and making sure they have comparable characteristics substan the performance of any econometric program evaluation estimator. We show how t analysis to estimate the impact of treatment on the treated using ordinary obser
934 citations
04 May 1998
TL;DR: A hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations is described that shows selection to provide an advantage for some (but not all) complex functions.
Abstract: This paper describes a evolutionary optimization algorithm that is a hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations. A comparison is performed between the hybrid swarm and the ordinary particle swarm that shows selection to provide an advantage for some (but not all) complex functions.
897 citations
TL;DR: A new approach is proposed, and the positive and negative aspects of the application of GA in selecting variables for a partial least squares (PLS) model are taken into account, showing that this technique almost always produces very good results.
02 Jun 1998
TL;DR: In this article, the authors proposed alternative ways of weighting the estimation of a two-level model by using the reciprocals of the selection probabilities at each stage of sampling, and demonstrated that the variance estimators perform extremely well.
Abstract: When multilevel models are estimated from survey data derived using multistage sampling, unequal selection probabilities at any stage of sampling may induce bias in standard estimators, unless the sources of the unequal probabilities are fully controlled for in the covariates. This paper proposes alternative ways of weighting the estimation of a two-level model by using the reciprocals of the selection probabilities at each stage of sampling. Consistent estimators are obtained when both the sample number of level 2 units and the sample number of level 1 units within sampled level 2 units increase. Scaling of the weights is proposed to improve the properties of the estimators and to simplify computation. Variance estimators are also proposed. In a limited simulation study the scaled weighted estimators are found to perform well, although non-negligible bias starts to arise for informative designs when the sample number of level 1 units becomes small. The variance estimators perform extremely well. The procedures are illustrated using data from the survey of psychiatric morbidity.
Patent•
14 Sep 1998
TL;DR: A slot machine is configured to have a main game comprising a video reel slot arrangement with at least five reels and at least nine paylines and a secondary event selection game consisting of a selection game as discussed by the authors.
Abstract: A slot machine is configured to have a main game comprising a video reel slot arrangement with at least five reels and at least nine paylines and a secondary event selection game comprising a selection game. Whenever the player achieves a combination of symbols on the main game that awards the player with the opportunity to play the secondary event selection game, the number of chances provided to the player to play the secondary event game is based on the number of paylines that the player has played on the main game or by the number of credits wagered on each payline. Alternatively, the number of selections awarded to the player on the secondary event game can be determined by the symbol combinations achieved by the player on the main game. All selections by the player in the secondary event selection game can be winning selections or, alternatively, the selections in the secondary event selection game can be either winning or losing selections.
TL;DR: The approach presented here first considers the biology of the host, which host stages are attacked, and how the host is utilized by the parasitoid, and a similar thesis, that developmental problems faced by egg parasitoids influence how these oophages locate hosts, is presented.
01 Jan 1998
TL;DR: In this article, the authors propose to use the concept of Total Cost of Ownership as a basis for comparing vendor selection models and illustrate the comparison with a real life data set of the purchasing problem of ball bearings at Cockerill Sambre, a Belgian multinational company in the steel industry.
Abstract: Abstract Many different vendor selection models have been published in the purchasing literature. However there has been no systematic approach to compare the relative efficiency of the systems. In this paper we propose to use the concept of Total Cost of Ownership as a basis for comparing vendor selection models. We illustrate the comparison with a real life data set of the purchasing problem of ball bearings at Cockerill Sambre, a Belgian multinational company in the steel industry. From a Total Cost of Ownership perspective mathematical programming models outperform rating models and multiple item models generate better results than single item models for this specific case study.
TL;DR: It is argued that the logistic regression model is more suitable than linear regression for analyzing data from selection studies with dichotomous fitness outcomes, and it is shown that estimates of selection obtained can be transformed easily to values that directly plug into equations describing adaptive microevolutionary change.
Abstract: Understanding the mechanics of adaptive evolution requires not only knowing the quantitative genetic bases of the traits of interest but also obtaining accurate measures of the strengths and modes of selection acting on these traits. Most recent empirical studies of multivariate selection have employed multiple linear regression to obtain estimates of the strength of selection. We reconsider the motivation for this approach, paying special attention to the effects of nonnormal traits and fitness measures. We apply an alternative statistical method, logistic regression, to estimate the strength of selection on multiple phenotypic traits. First, we argue that the logistic regression model is more suitable than linear regression for analyzing data from selection studies with dichotomous fitness outcomes. Subsequently, we show that estimates of selection obtained from the logistic regression analyses can be transformed easily to values that directly plug into equations describing adaptive microevolutionary change. Finally, we apply this methodology to two published datasets to demonstrate its utility. Because most statistical packages now provide options to conduct logistic regression analyses, we suggest that this approach should be widely adopted as an analytical tool for empirical studies of multivariate selection.
Patent•
14 Jan 1998
TL;DR: In this article, RNA-protein fusion production methods which involve a high salt post-translational incubation step are described, and the authors describe a method for RNA-Protein fusion.
Abstract: Described herein are RNA-protein fusion production methods which involve a high salt post-translational incubation step.
TL;DR: In this paper, the authors show that an out-ranking approach may be very well suited as a decision-making tool for initial purchasing decisions, such as make-or-buy decisions and supplier selection.
TL;DR: The effects of selection by host immune responses on transmission dynamics was analyzed in a broad class of antigenically diverse pathogens, finding that strain structure is unstable, varying in a manner that is either cyclical or chaotic.
Abstract: The effects of selection by host immune responses on transmission dynamics was analyzed in a broad class of antigenically diverse pathogens. Strong selection can cause pathogen populations to stably segregate into discrete strains with nonoverlapping antigenic repertoires. However, over a wide range of intermediate levels of selection, strain structure is unstable, varying in a manner that is either cyclical or chaotic. These results have implications for the interpretation of longitudinal epidemiological data on strain or serotype abundance, design of surveillance strategies, and the assessment of multivalent vaccine trials.
TL;DR: How in certain situations two multi-criteria analysis tools, multi-objective programming and data envelopment analysis, can be used together for this selection and negotiation process with vendors who were not selected is described.
TL;DR: It was concluded that careful design of a selection algorithm should include consideration of spectral noise distributions in the input data to increase the likelihood of successful and appropriate selection for data with noise distributions resulting in large outliers.
Abstract: The mathematical basis of improved calibration through selection of informative variables for partial least-squares calibration has been identified. A theoretical investigation of calibration slopes indicates that including uninformative wavelengths negatively affect calibrations by producing both large relative bias toward zero and small additive bias away from the origin. These theoretical results are found regardless of the noise distribution in the data. Studies are performed to confirm this result using a previously used selection method compared to a new method, which is designed to perform more appropriately when dealing with data having large outlying points by including estimates of spectral residuals. Three different data sets are tested with varying noise distributions. In the first data set, Gaussian and log-normal noise was added to simulated data which included a single peak. Second, near-infrared spectra of glucose in cell culture media taken with an FT-IR spectrometer were analyzed. Finally, dispersive Raman Stokes spectra of glucose dissolved in water were assessed. In every case considered here, improved prediction is produced through selection, but data with different noise characteristics showed varying degrees of improvement depending on the selection method used. The practical results showed that, indeed, including residuals into ranking criteria improves selection for data with noise distributions resulting in large outliers. It was concluded that careful design of a selection algorithm should include consideration of spectral noise distributions in the input data to increase the likelihood of successful and appropriate selection.
TL;DR: Empirical studies on a particular safe regression test selection technique are reported, in which the technique is compared to the alternative regression testing strategy of running all tests, and indicate that it can be cost-effective, but that its costs and benefits vary widely based on a number of factors.
Abstract: Regression testing is an expensive testing procedure utilized to validate modified software. Regression test selection techniques attempt to reduce the cost of regression testing by selecting a subset of a program's existing test suite. Safe regression test selection techniques select subsets that, under certain well-defined conditions, exclude no tests (from the original test suite) that if executed would reveal faults in the modified software. Many regression test selection techniques, including several safe techniques, have been proposed, but few have been subjected to empirical validation. This paper reports empirical studies on a particular safe regression test selection technique, in which the technique is compared to the alternative regression testing strategy of running all tests. The results indicate that safe regression test selection can be cost-effective, but that its costs and benefits vary widely based on a number of factors. In particular, test suite design can significantly affect the effectiveness of test selection, and coverage-based test suites may provide test selection results superior to those provided by test suites that are not coverage-based.
TL;DR: An interplay among experimental studies of protein synthesis, evolutionary theory, and comparisons of DNA sequence data has shed light on the roles of natural selection and genetic drift in 'silent' DNA evolution.
TL;DR: The investigation of G×E interactions for grain yield of wheat in Australia has matured to the point where an understanding of some of their causes has enabled wheat breeders to exploit positive components of specific adaptation, indicating the importance of establishing a MET system that is relevant to the target population of environments of the breeding program.
Abstract: Genotype£environment (G£E) interactions complicate selection for broad adaptation, while their nature and causes need to be understood to utilise and exploit them in selection for specific adaptation. This invited review combines an assessment of the literature with the experience we have gained from involvement in wheat breeding and associated research programs to assess (1) the implications of G£E interactions for wheat breeding in Australia, (2) the impact that research into GXE interactions has had on breeding strategy, and (3) the evidence for impact from this research effort on genetic improvement of crop adaptation. The role of analytical methodology in this process is considered and some important issues are discussed. There are su±cient examples drawn from wheat breeding in Australia to suggest that progress in dealing with G£E interactions can be made and several of these are presented. They show that impact in plant breeding follows from achieving an appropriate level of understanding of the environmental and genetic factors causing the interactions as well as an assessment of their importance in the target genotype{environment system. An accurate definition of the environmental factor(s) contributing to the GXE interactions has been particularly important in determining the relevance of observed differences in plant adaptation to the target population of environments. From the combination of biological and statistical studies, a more comprehensive understanding of G£E interactions has emerged and contributed to new concepts and procedures for dealing with them. Distinguishing between what are repeatable and non-repeatable interactions is a key step. Genuine cases of positive specific adaptation observed in multi-environment trials (METs) can be exploited by appropriately targeted selection strategies, while non-repeatable interactions are accommodated by selection for broad adaptation. The investigation of GXE interactions for grain yield of wheat in Australia has matured to the point where an understanding of some of their causes has enabled wheat breeders to exploit positive components of specific adaptation. The experience that has been gained in achieving these advances indicates the importance of establishing a MET system that is relevant to the target population of environments of the breeding program. The investment of adequate resources into effective design, conduct, analysis, and interpretation of METs remains critical to continued progress from selection in complex genotype{environment systems that present large GXE interactions. Wheat breeders who understand their genetic material and the target population of environments can then use the generated information base to achieve impact from their breeding programs.
TL;DR: This model provides a possible evolutionary explanation for the ubiquity of large genetic correlations between maternal and offspring traits, and suggests that this pattern of coinheritance may reflect functional relationships between these characters (i.e., functional integration).
Abstract: Parents often have important influences on their offspring's traits and/or fitness (i.e., maternal or paternal effects). When offspring fitness is determined by the joint influences of offspring and parental traits, selection may favor particular combinations that generate high offspring fitness. We show that this epistasis for fitness between the parental and offspring genotypes can result in the evolution of their joint distribution, generating genetic correlations between the parental and offspring characters. This phenomenon can be viewed as a coadaptive process in which offspring genotypes evolve to function with the parentally provided environment and, in turn, the genes for this environment become associated with specific offspring genes adapted to it. To illustrate this point, we present two scenarios in which selection on offspring alone alters the correlation between a maternal and an offspring character. We use a quantitative genetic maternal effect model combined with a simple quadratic model of fitness to examine changes in the linkage disequilibrium between the maternal and offspring genotypes. In the first scenario, stabilizing selection on a maternally affected offspring character results in a genetic correlation that is opposite in sign to the maternal effect. In the second scenario, directional selection on an offspring trait that shows a nonadditive maternal effect can result in selection for positive covariances between the traits. This form of selection also results in increased genetic variation in maternal and offspring characters, and may, in the extreme case, promote host-race formation or speciation. This model provides a possible evolutionary explanation for the ubiquity of large genetic correlations between maternal and offspring traits, and suggests that this pattern of coinheritance may reflect functional relationships between these characters (i.e., functional integration).
TL;DR: Weak and strong frequency dependence, as distinguished in this article, refer to two very different forms of selection.
Abstract: Frequency-dependent selection is so fundamental to modern evolutionary thinking that everyone `knows' the concept. Yet the term is used to refer to different types of selection. The concept is well defined in the original context of population genetics theory, which focuses on short-term evolutionary change. The original concept becomes ambiguous, however, when used in the context of long-term evolution, where density dependence becomes essential. Weak and strong frequency dependence, as distinguished in this article, refer to two very different forms of selection.