Browsing by Subject "voting"

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Open Access
Batch learning of disjoint feature intervals
(1996) Akkuş, Aynur
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.
Open Access
Benefit maximizing classification using feature intervals
(2002) İkizler, Nazlı
For a long time, classification algorithms have focused on minimizing the quantity of prediction errors by assuming that each possible error has identical consequences. However, in many real-world situations, this assumption is not convenient. For instance, in a medical diagnosis domain, misdiagnosing a sick patient as healthy is much more serious than its opposite. For this reason, there is a great need for new classification methods that can handle asymmetric cost and benefit constraints of classifications. In this thesis, we discuss cost-sensitive classification concepts and propose a new classification algorithm called Benefit Maximization with Feature Intervals (BMFI) that uses the feature projection based knowledge representation. In the framework of BMFI, we introduce five different voting methods that are shown to be effective over different domains. A number of generalization and pruning methodologies based on benefits of classification are implemented and experimented. Empirical evaluation of the methods has shown that BMFI exhibits promising performance results compared to recent wrapper cost-sensitive algorithms, despite the fact that classifier performance is highly dependent on the benefit constraints and class distributions in the domain. In order to evaluate costsensitive classification techniques, we describe a new metric, namely benefit accuracy which computes the relative accuracy of the total benefit obtained with respect to the maximum possible benefit achievable in the domain.