Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 3,045 papers for “Recursive partitioning”

recursive partitioning

2024

Citation: 'recursive partitioning' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.11503 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

Cited by 0

Recursive Partitioning

Brian S. Everitt, Torsten Hothorn · Encyclopedia of Statistical Sciences · 2004

Cited by 0Open access

Recursive partitioning

Douglas M. Hawkins · Wiley Interdisciplinary Reviews Computational Statistics · 2009

Abstract Recursive partitioning (RP) is a predictive approach with minimal statistical or model assumptions, which models the relationship in terms of trees or dendrograms. It is particularly appropriate for initial exploration of large data sets, especially messy ones, and may either validate other approaches with stronger model assumptions or lead to a final analysis in its own right. It may be used for either a nominal scale (categorical) or an interval‐scale (numeric) dependent variable. A major issue is the size of tree to be fitted; different approaches for this have been proposed. Like many other feature selection methods, RP is unstable, the model being potentially sensitive to minor perturbations in the data. Fitting multiple trees helps explore alternative models, and also provides better predictions than those giving by a single tree. RP is well supported in both commercial and public domain software. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Data Reduction, Smoothing, and Filtering

Cited by 11

Recursive Partitioning

Brian Everitt, Torsten Hothorn, L Breiman · Wiley StatsRef: Statistics Reference Online · 2014

Cited by 1Open access

Boosting, Recursive Partitioning

Claudio Conversano, Roberta Siciliano, Via Cintia et al. · 2012

In recent years, the explosion in computation and information technology allowed to dispose of vast amounts of data. Nowadays, the statistician’s job is to make sense of it all, by extracting important patterns and trends with the aim of understanding what is inside the data. The new challenge of learning from data has been called “statistical learning ” (Hastie et. al, 2001). In this framework, a lot of new tools have been introduced improving the performance of standard supervised methods. We present a combined semiparametric approach to supervised statistical learning problems called Generalized Additive Multi-Mixture Model (GAM-MM, Conversano, 2002). This is a nonlinear regression tool capable to capture the complexity of the data. It has been defined as an additive combination of mixtures of different classifiers (both parametric and nonparametric) associated to each feature, that uses a backfitting-like algorithm for the estimation/prediction of the output of the response variable. In this approach, Bagging (Brieman, 1996) and Boosting (Freund and Shapire, 1996) can be used to estimate the mixing parameters and to train the additive combination of

Cited by 0

Recursive Partitioning Using Ranks.

Mark R. Segal · Defense Technical Information Center (DTIC) · 1985

Replacing the conventional splitting rules used in constructing regression trees by rules based on two sample rank statistics affords many advantages and equally poses some problems. Among the former are computational ease, invariance under monotone transformations of the response and worthwhile extension to censored data. The difficulties involve devising good pruning strategies in the absence of within node loss. These are addressed using look-ahead, bottom-up techniques. Some real-world and simulation performances of the methodology are presented. (Author)

Cited by 2

RECURSIVE PARTITIONING ESTIMATION (CONTROL)

Ching-Fu Gau · Purdue e-Pubs (Purdue University System) · 1984

The partitioned solutions for adaptive control, estimation, identification, and associated Riccati equation solutions were developed by Lainiotis and his co-workers. In this work, the Recursive Partitioning Algorithm (RPA), a recursive version of Lainiotis' Generalized Partitioned Algorithm (GPA) is presented. The interpretation of RPA as a combination of a nominal Kalman filter for the partial initial condition and a recursive least-squares estimator for smoothing the remaining portion of a total initial condition gives great intuitive insight into the partitioned solutions of Lainiotis. The Parallel Partitioning Algorithm (PPA) is then presented to unify various partitioning approaches for optimal linear estimation. This procedure allows the process noise to be partitioned into two parts. The resulting algorithms based on the PPA retained a two-part partitioning structure which consists of two nested suboptimal kalman filters. The applications of RPA are presented, including a fast computational approach in optimal linear estimation, a dependent partitioning of initial condition, a multipartitioning of the initial condition, a decomposed parameter estimator, smoothing algorithms, change of initial condition for filtered and smoothed estimates, and a fixed-interval process-noise partitioning algorithm. In addition, the PPA is applied to obtain a decoupled bias-filtering algorithm for randomly time-varying bias system, a Q-adaptive estimator, and unknown function estimator, a decentralized optimal filter for large inter-connected system, and a two-stage filtering solutions which include partitioning of measurement noise. A unique two-filter approach to the problem of tracking of maneuvering targets is presented, which facilitates the switch between different filters. Finally, the applications to a multi-rate and a simplified filter are presented.

Cited by 0

Model-Based Recursive Partitioning

Achim Zeileis, Torsten Hothorn, Kurt Hornik · Journal of Computational and Graphical Statistics · 2008

Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a dataset; (2) test for parameter instability over a set of partitioning variables; (3) if there is some overall parameter instability, split the model with respect to the variable associated with the highest instability; (4) repeat the procedure in each of the daughter nodes. The algorithm yields a partitioned (or segmented) parametric model that can be effectively visualized and that subject-matter scientists are used to analyzing and interpreting.

Cited by 759

A Recursive Partitioning Approach to Improving Hypergraph Partitioning

Umair F. Siddiqi, Gary Gréwal, Shawki Areibi · 2024

Balanced hypergraph partitioning (BHP) is a fundamental combinatorial optimization problem in application specific integrated circuit (ASIC) and field-programmable gate array (FPGA) design flows. Multi-level partitioners (MLP) comprise a set of heuristics and are a popular way to solve BHP problems. The BHP problem becomes more complex when the number of partitions (K) is large. Recursive bi-partitioning is a traditional way to solve the problem for larger values of K. However, recursive bi-partitioning is usable for only a smaller number of partitions. This work proposes to generalize recursive bi-partitioning to recursive K-way partitioning to make recursive partitioning usable for all non-prime values of K. The practical usefulness of the proposed recursive method was evaluated by enhancing a recent MLP, TritonPart, and assessed the performance using Titan23 benchmarks. The proposed recursive method helped TritonPart to successfully solve problems with larger K values than it could solve without using the proposed method. Secondly, it also improved the runtime of TritonPart. A comparison with MT-KaHyPar showed it can find solutions with 80% lower cutsize. In short, the proposed recursive method is a useful way to enhance the capability of existing MLPs in terms of their runtime and ability to solve problems of larger K values.

Cited by 1

Recursive Partitioning for Survival Data

Ambra Macis · Institutional Research Information System (Università degli Studi di Brescia) · 2022

During the years many machine learning methods have been introduced for analyzing survival data. Among these, survival trees are a useful method for defining homogeneous groups according to their survival probability. In this context there are still some unclear points, both related to theoretical and practical issues in model fitting and performance evaluation. The aim of this contribution is to shed light on some of these points.

Cited by 0

Recursive partitioning to reduce distortion

Andrew B. Nobel · IEEE Transactions on Information Theory · 1997

Adaptive partitioning of a multidimensional feature space plays a fundamental role in the design of data-compression schemes. Most partition-based design methods operate in an iterative fashion, seeking to reduce distortion at each stage of their operation by implementing a linear split of a selected cell. The operation and eventual outcome of such methods is easily described in terms of binary tree-structured vector quantizers. This paper considers a class of simple growing procedures for tree-structured vector quantizers. Of primary interest is the asymptotic distortion of quantizers produced by the unsupervised implementation of the procedures. It is shown that application of the procedures to a convergent sequence of distributions with a suitable limit yields quantizers whose distortion tends to zero. Analogous results are established for tree-structured vector quantizers produced from stationary ergodic training data. The analysis is applicable to procedures employing both axis-parallel and oblique splitting, and a variety of distortion measures. The results of the paper apply directly to unsupervised procedures that may be efficiently implemented on a digital computer.

Cited by 20

Results from recursive partitioning models.

Raúl Abel Vaca (7430051), Duncan John Golicher (7430054), Rocío Rodiles-Hernández (7430057) et al. · Figshare · 2019

<p>Recursive partitioning decision tree based on all predictor variables: a) upper basin; b) middle basin; and c) lower basin. The probability that a given pixel is forested can be found as a series of binary decisions. The values used are relative indices. Direct beam radiation remains an important factor in addition to slope per se as is accessibility.</p>

Cited by 0Open access