Performance analysis of stacked generalization

Download

index.pdf

Date

2008

Author

Özay, Mete

Metadata

Show full item record

Item Usage Stats

128
views

84
downloads

Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. This study consists of two major parts. In the first part, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers and the content of the training data. In the second part, based on the findings for a new class of algorithms, called Meta-Fuzzified Yield Value (Meta-FYV) is introduced. The first part introduces and verifies two hypotheses by a set of controlled experiments to assure the performance gain for SG. The learning mechanisms of SG to achieve high performance are explored and the relationship between the performance of the individual classifiers and that of SG is investigated. It is shown that if the samples in the training set are correctly classified by at least one base layer classifier, then, the generalization performance of the SG is increased, compared to the performance of the individual classifiers. In the second hypothesis, the effect of the spurious samples, which are not correctly labeled by any of the base layer classifiers, is investigated. In the second part of the thesis, six theorems are constructed based on the analysis of the feature spaces and the stacked generalization architecture. Based on the theorems and hypothesis, a new class of SG algorithms is proposed. The experiments are performed on both Corel data and synthetically generated data, using parallel programming techniques, on a high performance cluster.

Subject Keywords

Electronic computers.

URI

http://etd.lib.metu.edu.tr/upload/3/12609946/index.pdf
https://hdl.handle.net/11511/17874

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes Vural, Hülya; Alpaslan, Ferda Nur; Department of Computer Engineering (2004) The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
On the performance of Stacked Generalization classifiers Ozay, Mete; Yarman Vural, Fatoş Tunay (2008-06-27) Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique performs better than the individual classifiers. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. In this work, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual...
Recursive shortest spaning tree algorithms for image segmentation Bayramoğlu, Neslihan Yalçın; Bazlamaçcı, Cüneyt Fehmi; Department of Electrical and Electronics Engineering (2005) Image segmentation has an important role in image processing because it is a tool to obtain higher level object descriptions for further processing. In some applications such as large image databases or video image sequence segmentations, the speed of the segmentation algorithm may become a drawback of the application. This thesis work is a study to improve the run-time performance of a well-known segmentation algorithm, namely the Recursive Shortest Spanning Tree (RSST). Both the original and the fast RSST...
Implementation of concurrent constraint transaction logic and its user interface Altunyuva, Fethi; Karagöz, Pınar; Department of Computer Engineering (2006) This thesis implements a logical formalism framework called Concurrent Constraint Transaction Logic (abbr.,CCTR) which was defined for modeling and scheduling of workflows under resource allocation and cost constraints and develops an extensible and flexible graphical user interface for the framework. CCTR extends Concurrent Transaction Logic and integrates with Constraint Logic Programming to find the correct scheduling of tasks that involves resource and cost constraints. The developed system, which integ...
Category knowledge, skeleton-based shape matching and shape classification Erdem, İbrahim Aykut; Tarı, Zehra Sibel; Department of Computer Engineering (2008) Skeletal shape representations, in spite of their structural instabilities, have proven themselves as effective representation schemes for recognition and classification of visual shapes. They capture part structure in a compact and natural way and provide insensitivity to visual transformations such as occlusion and articulation of parts. In this thesis, we explore the potential use of disconnected skeleton representation for shape recognition and shape classification. Specifically, we first investigate th...

Citation Formats

M. Özay, “Performance analysis of stacked generalization,” M.S. - Master of Science, Middle East Technical University, 2008.