https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Wozniak%20-%20Multiple%20Classifier%20Systems%20-%202014.pdf

A survey of multiple classifier systems as hybrid systems

The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates

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Text-Independent Writer Identification and Verification Using Textural and Allographic Features

Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets.

/pdf/cost-sensitive-decision-tree-ensembles-for-effective-4bnfavt4u7.pdf

Cost-sensitive decision tree ensembles for effective imbalanced classification

In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO/sup 3/s) in uppercase handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the uppercase character set. Using a codebook of CO/sup 3/s from an independent training set of 100 writers, the probability-density function (PDF) of CC's was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO/sup 3/ PDF for identifying individual writers on the basis of a single sentence of uppercase characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO/sup 3/ PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.

/pdf/automatic-writer-identification-using-connected-component-47zkez7bkx.pdf

Automatic writer identification using connected-component contours and edge-based features of uppercase Western script

Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still missing and achieved improvements are inconsistent. By introducing different categories of classifier combinations in this review we attempt to put forward more specific directions for future theoretical research. We also introduce a retraining effect and effects of locality based training as important properties of classifier combinations. Such effects have significant influence on the performance of combinations, and their study is necessary for complete theoretical understanding of combination algorithms.

Review of Classifier Combination Methods

In pattern recognition, there is a growing use of multiple classifier combinations with the goal to increase recognition performance. In many cases, plurality voting is a part of the combination process. In this article, we discuss and test several well known voting methods from politics and economics on classifier combination in order to see if an alternative to the simple plurality vote exists. We found that, assuming a number of prerequisites, better methods are available, that are comparatively simple and fast.

An overview and comparison of voting methods for pattern recognition

This paper introduces the WANDA Measurement tool (WAM) for forensic document examination. The WAM is an essential component of WANDA, a workbench that supports the user in the complete task flow of processing documents, measuring characteristic features in handwritten documents, and writer search. By using technologies like plug-ins. XML, and client/server modularity, a system was created that is easy to maintain. portable, and highly adaptable. Within WANDA, the WAM is the tool for interactively measuring handwriting features. The WAM was developed based on recommendations from a comparison study between two forensic writeridentification systems, Script and FISH. It incorporates nine measurements identical to those of FISH, and a new allograph measurement that is discussed in this paper. Furthermore, its intuitive new user-interface reduces the steep learning curve and streamlines the working process. A comparison of features previously measured by forensic experts using FISH, with measurements obtained through WANDA, assessed the precision of the WAM. It has shown that the small deviations yielded fall well within the possible imprecision caused by scanning or preprocessing operations, and far below the standard deviation of FISH measurements. Finally, results from usability tests with expert and novice users show that the WAM is easy to use.
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The WANDA Measurement Tool for Forensic Document Examination

This paper evaluates the performance of edge-based directional probability distributions as features in writer identification in comparison to a number of nonangular features. It is noted that angular features outperform all other features. However, the nonangular features provide additional valuable information. Rank-combination was used to realize a sparse-parametric combination scheme based on nearest-neighbor search. Limitations of the proposed methods pertain to the amount of handwritten material needed in order to obtain reliable distribution estimates. The global features treated in this study are sensitive to major style variation (upper- vs lower case), slant, and forged styles, which necessitates the use of other features in realistic forensic writer identification procedures.

Sparse-parametric writer identification using heterogeneous feature groups

This paper presents the WANDA Workbench, which is an open frame-work for electronic data processing. The framework provides generic interfaces for 'plug-in' applications for graphical user interfaces (client desktop with client plug-ins) and processing modules (server with server plug-ins). The applied plug-in concept allows for the functional extension of the workbench without changing the framework. Moreover, for data modeling, messaging and system configuration the eXtensible Markup Language (XML) is implemented, which supports the interoperability with existing applications and allows for continuous adaptations to the evolving state of technology.
The current application domain of the framework is writer identification and handwriting examination as frequently used in crime investigation and prosecution. Dedicated plug-ins using digital image processing and pattern recognition establish objective measurements and feature extraction to promote reproducible analysis results. Moreover, based on domain knowledge, which was collected from forensic document examiners, the data standard WandaXML was designed that allows for the annotation, processing, journaling and storage of digitized questioned handwriting documents.

/pdf/wanda-a-common-ground-for-forensic-handwriting-examination-2awibf42i1.pdf

M. van Erp

Papers

An overview and comparison of voting methods for pattern recognition

The WANDA Measurement Tool for Forensic Document Examination

Sparse-parametric writer identification using heterogeneous feature groups

WANDA: A common ground for forensic handwriting examination and writer identification

The WANDA Measurement Tool for Forensic Document Examination