5 research outputs found
Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals
Cataloged from PDF version of article.A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records, the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time. (C) 1998 Elsevier Science B.V. All rights reserved
Batch learning of disjoint feature intervals
Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1996.Thesis (Master's) -- Bilkent University, 1996.Includes bibliographical references leaves 98-104.This thesis presents several learning algorithms for multi-concept descriptions
in the form of disjoint feature intervals, called Feature Interval Learning algorithms
(FIL). These algorithms are batch supervised inductive learning algorithms,
and use feature projections of the training instances for the representcition
of the classification knowledge induced. These projections can be generalized
into disjoint feature intervals. Therefore, the concept description learned
is a set of disjoint intervals separately for each feature. The classification of
an unseen instance is based on the weighted majority voting among the local
predictions of features. In order to handle noisy instances, several extensions
are developed by placing weights to intervals rather than features. Empirical
evaluation of the FIL algorithms is presented and compared with some other
similar classification algorithms. Although the FIL algorithms achieve comparable
accuracies with other algorithms, their average running times are much
more less than the others.
This thesis also presents a new adaptation of the well-known /s-NN classification
algorithm to the feature projections approach, called A:-NNFP for
k-Nearest Neighbor on Feature Projections, based on a majority voting on individual
classifications made by the projections of the training set on each
feature and compares with the /:-NN algorithm on some real-world and cirtificial
datasets.Akkuş, AynurM.S
Non-incremental classification learning algorithms based on voting feature intervals
Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 147-154.Demiröz, GülşenM.S