## 3 Proposed Work
### State Space Model
The \"Signature of Histograms of Orientations (SHOT)\" is another type of descriptor that is dependent on a spherical grid centered at point X, which is used to construct the descriptor. However, it is rare to understand a single entry in the resulting feature vector for any of these approaches. ### Feature Selection

To handle a large number of features or training cases, it is often desirable to use simpler and more efficient procedures. Therefore, filter-based approaches are commonly employed to choose a subset of related features [44]. These filter-based techniques focus on relationships between features and classes that are clearer and easier to understand, as well

\[\mathrm{P}(m/z_{1:t})=\frac{p(\mathrm{m}/z_{1:t-1})\mathrm{p}(Z_{t}/m)}{P(Z_{t} /Z_{1:t-1})} \tag{3}\]0 SS Process flow. as possible relationships within features (such as relationships depending on better known concepts such as distance, consistency, dependency, or information), while embedded approaches use more complicated relationships that are harder to understand [45]. Because each score function focuses on reducing the classification error, it could be argued that embedding methods are more appropriate for statistical learning or machine learning, as they present these interactions as score functions. Further, embedded methodologies would immediately debrief a dependence between the settings of a classifier and selected features, such as the number and type of weak learners used and the number of (preferably high) choices taken into account for each variable. To avoid extensive classifier adjustment and maintain their applicability for non-expert users, we concentrate on filter-based approaches and are willing to agree that these approaches generally produce slightly lower performance. We will briefly discuss the fundamental ideas behind filter-based techniques for both univariate and multivariate analysis in the following sections. It is significant to specify that when using these methods, the data should be trained with an equal number of training samples for each class to avoid bias in feature selection. ### Classification

Most approaches tend to concentrate on supervised classification schemes for various applications. The basic idea is to train a classifier with previously provided training data so that it can adapt to unseen data. As a result, the testing data are typically depicted by a set of testing samples, X, where each sample contains a class label and a feature vector in a d-dimensional feature space. On the other hand, the test set, Y, typically consists of new data that need to be classified and feature vectors in the d-dimensional feature space. We focus on individual point categorization due to its simplicity, efficiency, and reproducibility, as well as the availability of numerous suitable algorithms in different software packages. In order to determine the relationship between feature vectors and class labels during the training phase, multiple learning principles may be involved. Therefore, we use various classifiers and provide a concise overview of the most significant concepts in the following subsections. Here, we consider the possibility that the training procedure may frequently be negatively impacted by an uneven splitting of training examples by class in the training set. To prevent this, we provide class re-balancing, which lowers error rates by randomly choosing the similar number of training samples for each class. End users will, as a result, not only understand how each approach works, but they will also be able to compare everything. A classifier Is provided with either all or just the selected features in the final step, and it assigns them to one of the specified (semantic) classes. Most approaches concentrate on supervised classification schemes for a diverse range of applications. The basic idea is to teach a classifier by utilizing existing training data, so that it can classify new information.