Abstract
Abstract In the data mining field, classification and prediction are one of the most major issues. In resent research, there are many methodologies to apply to classification problem, like Neural Network, Support Vector Machine, Mahalanobis Distance, Decision Tree and so on. Due to the powerful pattern recognition and error tolerance ability in neural network, neural network is usually applied to do the classification works. Before using neural network to do classification, there are significant influence on data preprocessing. Except scaling and normalization before using neural network, it doesn’t do additional process on data. All the attributes are regarded as the equal weight. If using irrelevant attributes to do the classification, neural network may misdirect by the irrelevant attributes. Therefore, neural network doesn’t do well on classification. So, in the data preprocessing, the weight of the attribute should be distinguish between different weights based on the classification results. It will highlight the attributes that have significant influence on outcome and the result of classification can increase. The calculation of the Mahalanobis Distance is used to measure the distance of instances, but it is rarely to calculate the distance between attributes. Calculate the Mahalanobis Distances between attributes can know which attributes have significant influence on classification result. Our research use Neural Network based on Transposed Weighted Mahalanobis Distance (TWMD-based NN) to solve the classification problems. Use the concept of the similarity, the bigger Mahalanobis Distance, the smaller the weight. Finally, use the data which processed by attribute weight to train Neural Network. The research results show that the processed data by the attribute weights are better than the original data. Keywords：Mahalanobis Distance、Neural Network、Feature weight