Kaberi Das

Removal and interpolation of missing values using wavelet neural network for heterogeneous data sets

Missing data are common occurrences and can have a significant effect on the conclusions that can be drawn from the data. In statistics, missing data or missing values occur when no data value is stored for the variable in the current observation. Due to missing value we are facing several problems like information loss for computation and analysis of data. Missing values can also cause misleading results by introducing bias. Serious bias is a systematic difference between the observed and the unobserved data. This paper focuses on a methodological framework for the development of an automated data imputation model based on wavelet neural network (WNN). Here we use an adaptive higher order functions or different wavelet functions as the kernel of NN instead of each neuron activation function. A wavelet is a wavelike oscillation with a amplitude that starts out at zero, increases, and then decreases back to zero. Generally, wavelets are purposefully crafted to have specific properties that make them useful for signal processing. Six real, integer and simulated data sets are exposed to a perturbation experiment, based on the random generation of missing values. Here neural network (NN) and WNN is applied in glass identification, wine recognition, heart disease, leukemia, breast cancer and lung cancer data set to find the missing value and compared with different classic imputation procedures. The experiment conducted considering different performance measures using WNN, not only improves the quality of a database with missing value but also the best results are clearly obtained with different variables.

Impact of learning algorithms on gene expression data set

Classification is a process which plays a vital role in the analysis of the gene expression data set. The paper focuses on variety of learning algorithms which are really challenging in nature. The proposed model has been implemented and evaluated by using 5 benchmark datasets and to evaluate the performance and throughput of the model, various learning algorithms has been used like Random Forest, Support vector Machine, K-Nearest Neighbor, Bayesian, Linear Discriminate, Multi layer Perception and Decision Tree. We proposed model by using the k –fold cross validation for training and testing of the data.

Sampling correctly for improving classification accuracy: a hybrid higher order neural classifier (HHONC) approach

Data sets contain very large amount of information, which is not an easy task for the users to scan the entire data set. The researchers initial task is to formulate a realistic explanation for the use of sampling in his research. Sampling has been often suggested as an effective tool to reduce the size of the dataset operated at some cost to accuracy. It is the the process of selecting a representative part of a data set for the purpose of determining parameters or characteristics of the whole data set. Due to sampling we overcome the problems like; i) in research it is not possible to collect and test each and every element from the data base individually; and ii) study of sample rather than the entire dataset is also sometimes likely to produce more reliable results. This paper focuses on different types of sampling strategies applied on hybrid higher order neural network classifier (HHONC) rather than artificial neural network which is having several limitations. To overcome such limitations HHONC have been used. Here sampling technique has been applied on four real, integers and categorical dataset such as breast cancer, pima Indian diabetes, leukaemia and lung cancer data set prior to classification. The main objective of this paper is an empirical comparison of different sampling strategies for classification which gives more accuracy.