A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.

SimulaD: A Novel Feature Selection Heuristics for Discrete Data

By applying a running average (with a window-size= d), we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. this optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.

Fusion of Multi-Modality Biomedical Images Using Deep Neural Networks

With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, a novel image fusion model is designed to fuse multi-modality medical images. The proposed model model suffers from hyper-parameters tuning issue, therefore, a multi-objective differential evolution (MDE) is used to optimize the initial parameters of the proposed model. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. The performance of the proposed model is validated by comparing the proposed model models with nine competitive models over fifteen benchmark images. Performance analysis reveal that the proposed model outperforms the competitive fusion models.

SimulaD: A Novel Feature Selection Heuristics For Discrete Data

For discrete big data which have a limited range of values, Conventional machine learning methods cannot be applied because we see clutter and overlapping of classes in such data: many data points from different classes overlap. In this paper we introduce a solution for this problem through a novel heuristics method. By applying a running average (with a window-size= d) we could transform Discrete data to broad-range, Continuous values. When we have more than 2 columns and one of them is containing data about the tags of classification (Class Column), we could compare and sort the features (Non-class Columns) based on the R2 coefficient of the regression for running averages. The parameters tuning could help us to select the best features (the non-class columns which have the best correlation with the Class Column). “Window size” and “Ordering” could be tuned to achieve the goal. This optimization problem is hard and we need an Algorithm (or Heuristics) for simplifying this tuning. We demonstrate a novel heuristics, Called Simulated Distillation (SimulaD), which could help us to gain a somehow good results with this optimization problem.