Accurate forecasting of the undecided population in a public opinion poll

Abstract

The problem of pollsters is addressed which is to forecast accurately the final answers of the undecided respondents to the primary question in a public opinion poll. The task is viewed as a pattern-recognition problem of correlating the answers of the respondents to the peripheral questions in the survey with their primary answers. The underlying pattern is determined with a supervised artificial neural network that is trained using the peripheral answers of the decided respondents whose primary answers are also known. With peripheral answers as inputs, the trained network outputs the most probable primary response of an undecided respondent. For a poll conducted to determine the approval rating of the (former) Philippine president, J. E. Estrada in December 1999 and March 2000, the trained network predicted with a 95% success rate the direct responses of a test population that consists of 24.57% of the decided population who were excluded in the network training set. For the undecided population (22.67% of December respondents; 23.67% of March respondents), the network predicted a final response distribution that is consistent with the approval/disapproval ratio of the decided population.

Statistical Feature-Based Craquelure Classification

“…We use the standard gradient descent method in training a three-layer NN with linear activation function in the input nodes, and f ðxÞ ¼ 1:7159 tanh(2/3 x) in the hidden and output nodes closely following the procedure of our previous works. 11,15,16 The mentioned architecture class and training algorithms have been successful in characterizing various complex systems problems, from hit songs prediction 11 to public opinion forecast 15 to signal classification. 16 After extensibly playing with the networks free parameters such as learning rate, hidden nodes, and various activation functions, we have not seen a marked improvement in the test set accuracy of NN as compared to the LDA procedure.…”

Characterizing the effect of seating arrangement on classroom learning using neural networks

“…The bias (input assigned as H11) is a common addition to NN training and serves as a fine-tuning parameter for better solution estimate [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

“…Here, h and a (respectively referred to as the learning and momentum rate) are adaptively varied to hasten learning [14][15][16][17][18][19][20]27]. Shown in Figure 2(c) is a flow diagram of how the training procedure is implemented.…”

“…In this article, using actual experiments and an artificial neural network (NN) we quantify the information transmitted during SIO as a result of various SA. The use of NN is guided by its previous successes in finding nonlinear relationships between variables in a wide variety of complex systems such as political opinions forecasts [14,15], noisy image recognition [16,17] and feature identification in different kinds of signals [18,19], propagation of light in a nonlinear medium [20,21], organization of competing biological species [22], stock trading and securities [23,24], financial management fraud [25], evolution of N-body [26], among others. The use of NN is preceded by first developing a set of lectures modified from Mazur's PI method [1,2].…”

Dendritic growth model of multilevel marketing

“…Artificial neural networks and dendritic cell algorithms, to name a few, have been successfully utilized to aid in the development of tools and/or understanding of robotics [1], finance [2], artificial immunology [3], public opinion [4], differential equations [5,6], and physics education [7]. Here, we demonstrate that dendritic growth model in a confined substrate can be used to capture the empirical features of multilevel marketing (MLM) schemes.…”