Algorithms reducing the storage requirement of the nearest neighbor classifier (NNC) can be divided into three main categories: Fast searching algorithms, Instance-based learning algorithms and Prototype based algorithms. In this paper an algorithm, called LVQPRU, is proposed for pruning NNC prototype vectors so that a compact classifier with good performance can be obtained. The basic condensi...

Multi-layer perceptrons (MLP) make powerful classifiers that may provide superior performance compared with other classifiers, but are often criticized for the number of free parameters. Most commonly, parameters are set with the help of either a validation set or cross-validation techniques, but there is no guarantee that a pseudo-test set is representative. Further difficulties with MLPs incl...

From a machine learning perspective, the analysis of gene expression data is complicated by the extremely large feature dimensionality. The presence of a large number of irrelevant features—here genes—makes such analysis prone to due to the curse of dimensionality. To overcome this limitation, Support Vector Machines (SVM) are widely employed, since it is well known that they possess good gener...

Classification of text documents become a need in today’s world due to increase in the availability of electronic data over internet. Till now, no text classifier is available for the classification of Punjabi documents. The objective of the work is to find best Punjabi Text Classifier for Punjabi language. Two new algorithms, Ontology Based Classification and Hybrid Approach (which is the comb...

This paper presents a new hybrid classifier that combines the probability based Bayesian Network paradigm with the Nearest Neighbor distance based algorithm. The Bayesian Network structure is obtained from the data by using the K2 structural learning algorithm. The Nearest Neighbor algorithm is used in combination with the Bayesian Network in the deduction phase. For those data bases in which s...

Pattern recognition systems are increasingly being used in adversarial environments like network intrusion detection, spam filtering and biometric authentication and verification systems, in which an adversary may adaptively manipulate data to make a classifier ineffective. Current theory and design methods of pattern recognition systems do not take into account the adversarial nature of such k...

Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class association rules (CARs). A very popular method of ordering rules for selection is based on confidence, support and antecedent size (CSA). Other methods are ...

A hybrid neural network and tree classification system for handwritten numeral recognition is proposed. The recognition system consists of coarse and fine classification based on a variety of stable and reliable global features and local features. For the coarse classifier: a four-layer feed forward neural networks with back propagation learning algorithm is employed to distinguish six subsets ...