Handling Missing Values via a Neural Selective Input Model

Abstract

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.

A Comparison of Different Estimation Methods to Handle Missing Data in Explanatory Variables

“…61, No. 12, pp: 3327-3336 2223 L ( ……(11) This leads to ECM algorithm in two steps, as those in the EM [10]. E-step is maintained whereas M-Step is replaced in several CM-steps (as in 11 and 12) to maximize CM-steps to find the expected complete-data [2].…”

Predicting Student Failure in an Introductory Programming Course with Multiple Back-Propagation

“…To deal with missing data a Neural Selective Input Model (NSIM) was proposed in [19], such that the act of obtaining the value of , represented by a random variable, ∼ Be(qj) (with Bernoulli distribution), is taken into consideration. To that end, the values are transformed by ( 5):…”