208 research outputs found

    Computer Literacy for Life Sciences: Helping the Digital-Era Biology Undergraduates Face Today's Research

    Get PDF
    Computer literacy plays a critical role in today's life sciences research. Without the ability to use computers to efficiently manipulate and analyze large amounts of data resulting from biological experiments and simulations, many of the pressing questions in the life sciences could not be answered. Today's undergraduates, despite the ubiquity of computers in their lives, seem to be largely unfamiliar with how computers are being used to pursue and answer such questions. This article describes an innovative undergraduate-level course, titled Computer Literacy for Life Sciences, that aims to teach students the basics of a computerized scientific research pursuit. The purpose of the course is for students to develop a hands-on working experience in using standard computer software tools as well as computer techniques and methodologies used in life sciences research. This paper provides a detailed description of the didactical tools and assessment methods used in and outside of the classroom as well as a discussion of the lessons learned during the first installment of the course taught at Emory University in fall semester 2009

    Epidemiologic Studies in Child and Adolescent Psychiatry: A Review of Methodology

    Get PDF
    Childhood psychiatric disorders are estimated to influence about 9 to 21% of relevant age group and interest in this disorders are increasing all over the world. The growing need to child and adolescent mental health leads the task of establishing proposals and policies in this field to become a priority for governments. The first step of such proposals should be determination of prevalence of child and adolescent mental disorders in that country. However, several major methodological problems make it hard to provide accurate prevalence estimates from epidemiological studies. Most common problems are within the fields of sampling, case definition, case ascertainment and data analyses. Such issues increases the costs of studies and hinder to reach large sample sizes. To minimize these problems, investigators have to be careful on choosing the appropriate methodology and diagnostic tools in their studies. Although there are many interviews and questionnaires for screening and diagnosing in child and adolescent psychiatry, only a few of them are suitable for epidemiological research. In parallel with the improvement in all fields of child and adolescent mental health in our country, some of the major screening and diagnosing tools used in prevalence studies in literature have already been translated and validated in Turkish. Most important of this tools for screening purposes are Child Behavior Checklist and Strengths and Difficulties Questionnaire and for diagnosing purposes are Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and Development and Well-Being Assessment. The aims of this article are to review the methodological problems of epidemiologic studies in child and adolescent psychiatry and to briefly discuss suitable diagnostic tools for extended sampled epidemiologic studies in our country

    A classification learning algorithm robust to irrelevant features

    Get PDF
    Presence of irrelevant features is a fact of life in many realworld applications of classification learning. Although nearest-neighbor classification algorithms have emerged as a promising approach to machine learning tasks with their high predictive accuracy, they are adversely affected by the presence of such irrelevant features. In this paper, we describe a recently proposed classification algorithm called VFI5, which achieves comparable accuracy to nearest-neighbor classifiers while it is robust with respect to irrelevant features. The paper compares both the nearest-neighbor classifier and the VFI5 algorithms in the presence of irrelevant features on both artificially generated and real-world data sets selected from the UCI repository

    Application of AHP to multicriteria inventory classification

    Get PDF
    Ankara : The Faculty of Management and Graduate School of Business Administration of Bilkent Univ., 1993.Thesis (Master's) -- Bilkent University, 1993.Includes bibliographical references leaves 65-67.In this thesis, a new method based on the application of Analytic Hierarchy Process (AHP) to ABC inventory classification is investigated. The traditional ABC classification method utilizes only the unit price and the annual usage of inventory items. However, in some cases, the classification done using only these two criteria turns out to be insufficient. The method described in this thesis enables the integration of several criteria that can be organized in a hierarchy into ABC classification. The method can be summarized as follows: A matrix is constructed by the pairwise comparison of criteria on the highest level. The elements of the eigen vector of this matrix represent the weights (priorities) of the criteria. If a criterion has subcriteria in the hierarchy, the weights computed in the similar manner for the subcriteria are multiplied by the weight of the criterion and inserted in its place. Repetition of these steps for aU levels of the hierarchy, the weight of all criteria are determined. Using the criteria weights determined by the AHP technique, the weighted score of each inventory item is computed. The items sorted by that weighted score are grouped in three classes: A, B, and C, as in the classical ABC classification. This new method is applied to the classification of inventory items used in rock excavation jobs done using blasting by a construction company. The same inventory is also classified according to the classical ABC technique, and the results are compared.Güvenir, NurayM.S

    Learning problem solving strategies using refinement and macro generation

    Get PDF
    In this paper we propose a technique for learning efficient strategies for solving a certain class of problems. The method, RWM, makes use of two separate methods, namely, refinement and macro generation. The former is a method for partitioning a given problem into a sequence of easier subproblems. The latter is for efficiently learning composite moves which are useful in solving the problem. These methods and a system that incorporates them are described in detail. The kind of strategies learned by RWM are based on the GPS problem solving method. Examples of strategies learned for different types of problems are given. RWM has learned good strategies for some problems which are difficult by human standards. © 1990

    Maximizing Benefit of Classifications Using Feature Intervals

    Full text link

    A novel hybrid approach for interestingness analysis of classification rules

    Get PDF
    Data mining is the efficient discovery of patterns in large databases, and classification rules are perhaps the most important type of patterns in data mining applications. However, the number of such classification rules is generally very big that selection of interesting ones among all discovered rules becomes an important task. In this paper, factors related to the interestingness of a rule are investigated and some new factors are proposed. Following this, an interactive rule interestingness-learning algorithm (IRIL) is developed to automatically label the classification rules either as "interesting" or "uninteresting" with limited user participation. In our study, VFP (Voting Feature Projections), a feature projection based incremental classification learning algorithm, is also developed in the framework of IRIL. The concept description learned by the VFP algorithm constitutes a novel hybrid approach for interestingness analysis of classification rules. © Springer-Verlag Berlin Heidelberg 2007

    An overview of regression techniques for knowledge discovery

    Get PDF
    Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5)

    Voting features based classifier with feature construction and its application to predicting financial distress

    Get PDF
    Voting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms. © 2009 Elsevier Ltd. All rights reserved

    Maximizing benefit of classifications using feature intervals

    Get PDF
    There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained
    corecore