A new data transformation technique based on mean and variance of individual gas-sensor combination has been applied to improve the classification accuracy of neural network classifier.
Despite repeated attempts using distinct input alignments, data-transformation techniques (i.e. identifying and removing rapidly evolving sites [ 6]) and amino acid substitution models, we were unable to obtain consistent tree topologies with high support for deep interior branch points.
The process of KDD consists of five major steps, namely the extraction goal, data selection, data transformation, data mining techniques, and finally, the interpretation of results, these steps are given in Fig. 1.
Modeling the probability density function (pdf) of non-Gaussian pressure is first addressed, followed by the simulation of pressure data through new static transformation techniques.
Applying both well-known and novel data transformation and data architecture techniques, we have arrived at a simple and elegant solution that achieves a significant data set size reduction and a dramatic increase in processing speed.
Explicit statements on data transformation and descriptions of the normalisation techniques applied prior to data analyses (e.g. classification) were not reported in 57 (37.5%) and 104 (68.4%) of the methodology papers respectively.
To tackle this problem and improve regression's accuracy, we propose a holistic problem-solving approach (named adaptive ridge regression system) integrating data transformation, multi-collinearity diagnosis, ridge regression technique and multi-objective optimization.
When a variable was highly skewed or heavily tailed, data transformation was used so that parametric statistical techniques could be conducted smoothly.
Data transformation was performed by applying a normalization technique to make all the data values fall within a small range (0 1) to enable the efficient performance of the predictive model (classifier).
