At present, most of the researches on feature selection do not consider the relevance between a term and its own category, the redundancy among terms. In order to solve this problem efficiently, we propose a new feature selection based on analyzing how to measure the relevance and the redundancy, which use Euclidean distance as the similarity calculation method. R2, the new feature selection al...

In order to study the effects of long-term selection on genetic parameters, the effect of selection on selected and correlated traits and to develop lines for various physiological, biochemical and molecular genetic studies a comprehensive selection study for body weight in chickens has been conducted at the Biotechnical Faculty of University in Ljubljana. Long-term divergent selection in chick...

Genetic drift is of considerable importance in most artificial selection experiments, which tend to have very small effective population sizes. We have previously considered the shortterm implications of drift in generating variance around the expected response (Chapter 6), which occurs even in the absence of major changes in allele frequencies. This chapter examines the implications for long-t...

The aim of this paper is to present deep neural network architectures and algorithms and explore their use in time series prediction. Existing and novel input variable selection algorithms and deep neural networks are applied for ultra-short-term wind prediction. Since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions, recent research e...

A method to predict long-term genetic contributions of ancestors to future generations is studied in detail for a population with overlapping generations under mass or sib index selection. An existing method provides insight into the mechanisms determining the flow of genes through selected populations, and takes account of selection by modeling the long-term genetic contribution as a linear re...

Class imbalance problem in data, plays a critical role in use of machine learning methods for text classification since feature selection methods expect homogeneous distribution as well as machine learning methods. This study investigates two different kinds of feature selection metrics (one-sided and two-sided) as a global component of term weighting schemes (called as tffs) in scenarios where...