Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Aligning sequences via an evolutionary tree: complexity and approximation

Abstract: We study the following fundamental problem in computational molecular biology: Given a set of DNA sequences representing some species and a phylogenetic tree depicting the ancestral relationship among these species, compute an optimal alignment oft he sequences by the means of constructing a minimum-cost evolutionary tree. The problem is an important variant of multiple sequence alignment, and is widely known as tree alignment. A more generalized version of the problem, called generalized tr’ee alignment in this paper, is that we are given the DNA sequences only and still have to construct a minimum-cost evolutionary tree. The paper presents some hardness results as well as approximation algorithms. It is shown that tree alignment is NP-hard and generalized tree alignment is MAX SNP-hard. On the positive side, we design an etiicient approximation algorithm with performance ratio 2 for tree alignment. The algorithm is then extended to a polynomialtime approximation scheme. The construction actually works for Steiner trees in any metric space, and thus implies a polynomial-time approximation scheme for planar Steiner trees under a given topology (with any constant degree). To our knowledge, this is the first polynomial-time approximation scheme in the fields of computational biology and Steiner trees. The contrast *Supported in part by a grant from SERB, McMaster University, and NSERC Operating Grant OGPO046613. Address: Department of Computer Science, MeMaster University, Hamilton, Ont. LSS 4K1, Canada. E-msil: .@@maccs.mcxnaster.ca t supported in p=t by us Dcp~trncnt of Energy Grant DE-FG03-90ER6099. Address: Computer Science Division, University of California, Berkeley, CA 94720, USA. Email: lawler@cs .berkele y. edu $supported in pmt by NSER.C operating Gr~t C) GPO046613. Address: Department of Electrical and Computer Engineering, McMaster University, Hsrnilton, Onterio L8S 4K1, Canada. B mail: lwsng@maccs .mcmaster .ca Permission to copy without fee all or pari of thk material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association of Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. STOC 945/94 Montreal, Quebee, Canada @ 1994 ACM 0-89791 -663-8/94/0005..$3.50 between the approximabtity of tree alignment and generalized tree alignment shows that a phylogenetic tree can indeed help in multiple alignment. The approximation algorithms may be useful in evolutionary genetics practice as they can provide a good initial alignment for the iterative method in [24].

Modeling of signal--response cascades using decision tree analysis

Abstract: Motivation: Signal transduction cascades governing cell functional responses to stimulatory cues play crucial roles in cell regulatory systems and represent promising therapeutic targets for complex human diseases. However, mathematical analysis of how cell responses are governed by signaling activities is challenging due to their multivariate and non-linear nature. Diverse computational methods are potentially available, but most are ineffective for protein-level data that is limited in extent and replication. Results: We apply a decision tree approach to analyze the relationship of cell functional response to signaling activity across a spectrum of stimulatory cues. As a specific example, we studied five intracellular signals influencing fibroblast migration under eight conditions: four substratum fibronectin levels and presence versus absence of epidermal growth factor. We propose techniques for preprocessing and extending the experimental measurement set via interpolative modeling in order to gain statistical reliability. For this specific case study, our approach has 70% overall classification accuracy and the decision tree model reveals insights concerning the combined roles of the various signaling activities in governing cell migration speed. We conclude that decision tree methodology may facilitate elucidation of signal--response cascade relationships and generate experimentally testable predictions, which can be used as directions for future experiments. Contact: [email protected]

One-Reason Decision Making Unveiled: A Measurement Model of the Recognition Heuristic.

Abstract: The fast-and-frugal recognition heuristic (RH) theory provides a precise process description of comparative judgments. It claims that, in suitable domains, judgments between pairs of objects are based on recognition alone, whereas further knowledge is ignored. However, due to the confound between recognition and further knowledge, previous research lacked an unbiased measure of RH use. Also, model comparisons have not been based on goodness-of-fit and model complexity as criteria. To overcome both limitations we introduce and test a multinomial processing tree model showing that it fits empirical data and provides an unbiased measure of RH use. Analyses of 8 data sets reveal that the RH alone cannot account for the data, not even when it is implemented in a probabilistic way. That is, information integration beyond recognition plays a vital role and cannot merely account for empirical data better due to model flexibility. Also, we present several validations of the central model parameter and provide demonstrations of how the model can be applied to study the less-is-more effect as well as determinants of (and individual differences in) RH use. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

Fuzzy SLIQ Decision Tree Algorithm

Abstract: Traditional decision tree algorithms face the problem of having sharp decision boundaries which are hardly found in any real-life classification problems. A fuzzy supervised learning in Quest (SLIQ) decision tree (FS-DT) algorithm is proposed in this paper. It is aimed at constructing a fuzzy decision boundary instead of a crisp decision boundary. Size of the decision tree constructed is another very important parameter in decision tree algorithms. Large and deeper decision tree results in incomprehensible induction rules. The proposed FS-DT algorithm modifies the SLIQ decision tree algorithm to construct a fuzzy binary decision tree of significantly reduced size. The performance of the FS-DT algorithm is compared with SLIQ using several real-life datasets taken from the UCI Machine Learning Repository. The FS-DT algorithm outperforms its crisp counterpart in terms of classification accuracy. FS-DT also results in more than 70% reduction in size of the decision tree compared to SLIQ.

Reliability and performance of tree-structured grid services

Abstract: Grid computing is a new emerging technology aiming at large-scale resource sharing, and global-area collaboration. It is a next step in an evolution of parallel and distributed computing. Due to the large scale and complexity of the grid system, its performance and reliability are difficult to model, analyse, and evaluate. This paper presents a model that relaxes some assumptions unsuitable for grid computing systems that have been made in the existed works studying the distributed systems. The paper proposes a virtual tree model of the grid service. This model simplifies the physical structure of a grid service, allows service performance (execution time) to be estimated, and takes into account the common cause failures in communication channels. Based on the model, an algorithm for evaluating the grid service performance distribution and the service reliability indices is suggested. The algorithm is based on graph theory, and Bayesian analysis. Illustrative examples are presented in which the results of the suggested algorithm are compared with simulation results.