Efficient pattern synthesis for nearest neighbour classifier

Abstract

Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality, but it has its own drawbacks. Most of the synthetic pattern generation techniques take more time than simple classification. In this paper, we propose a new strategy to reduce the time and memory requirements by applying prototyping as an intermediate step in the synthetic pattern generation technique. Results show that through the proposed strategy, classification can be done much faster without compromising much in terms of classification accuracy, in fact for some cases it gives better accuracy in lesser time. The classification time and accuracy can be balanced according to available memory and computing power of a system to get the best possible results.

Ensemble Neural Network in Classifying Handwritten Arabic Numerals

“…Table 3 shows the number of patterns classified correctly for each digit. Table 4 compares the accuracy of the proposed work with that of Ravindra Babu et al [7], Agrawal Monu et al, [4] and Vijaya et al, [19]. Compressed data in terms of runs [7] 92.47…”

“…To meet industry demands, handwritten digit recognition systems must have good accuracy, acceptable classification times, and robustness to variations in handwriting style. Currently several approaches are able to reach competitive performance in terms of accuracy, including the ones based on multilayer neural networks [1] [2] support vector machines [3] and nearest neighbor method [4]. Neural networks require huge amount of training data and time to term effective models, but their feedforward nature makes them very efficient during runtime.…”

“…To meet industry demands, handwritten digit recognition systems must have good accuracy, acceptable classification times, and robustness to variations in handwriting style. Monu Agrawal et al, [4] have proposed a strategy to reduce the time and memory requirements in handwritten recognition by applying prototyping as an intermediate step in the synthetic pattern generation technique. Vijayakumar et al, [9] have proposed a novel algorithm for recognition of handwritten digits by classifying digits into two groups.…”

Efficient Handwritten Numeral Recognition System Using Leaders of Separated Digit and RBF Network

“…More methods have been designed to minimize data size, computation and recognition [3,4,11,16,18]. classification of handwritten numerals need more memory and computation as Handwritten numerals data size is large.…”

“…Synthetic pattern recognition is used to overcome the dimensionality problem but it involves some drawback. Monu Agrawal et al [11] have proposed a new strategy to reduce time and memory requirements and also to overcome the drawback involved in synthetic pattern by applying prototype as an intermediate step in the synthetic pattern generation technique. An efficient hierarchical clustering algorithm is proposed by Vijaya et al [18] for effective clustering and prototype selection for pattern classification.This method uses incremental clustering principles to generate a hierarchical structure for finding the subgroups/subclusters with each cluster.…”