## 7 INCA Algorithm  
Increased system speed without reducing approach success is the most important aim of feature selection techniques. The literature on machine learning algorithms has been enhanced by several feature selection methods [40; 41; 42]. Specifically, the feature selection techniques reduce the computational cost of the deep learning algorithms having a lot of features. The performance of the feature selection approach on the feature set should be profoundly assessed [43]. However, in deep learning methods, the application of this analysis method is not time-saving. While the Minimum Redundancy Maximum Relevance (mRMR) technique performs better by using a non-parametric feature set, Principal Component Analysis and the Linear Discriminant analysis techniques outperform by applying a linear feature set [44]. For classification problems, current research mostly depends on feature extraction-based algorithms in terms of feature weight relations [45; 46; 47]. The NCA is one of the most popular features of importance-based selection algorithms since they offer a variety of classification strategies. Additionally, the computational time for these approaches outperforms the PCA and mRMR algorithms. Neighborhood Component Analysis (NCA), a method for feature selection and dimensionality reduction that is widely used in classification studies, is one of the most reliable supervised learning approaches for classifying multidimensional data into distinct classes [48; 49]. The classification tasks performed by NCA are carried out with learning vector optimization criteria related to the categorization accuracy performance of the nearest neighbor classifier. In particular, a linear projection chosen by NCA maximizes the projected area's performance of the nearest neighbor classifier. In NCA, training data with related class labels are applied to choose the projection that divides the classes effectively in the detected area. Nevertheless, the NCA makes assumptions about the distribution of each class, which are not reliable. It offers an equivalent fit to Gaussian mixtures for distribution modeling. To maximize the objective function \(F(w)\) for w, the regularized objective function is used in Equation (5). \[F(w)=\frac{1}{n}{\sum_{i=1}^{n}{P_{i}}}-\lambda \tag{5}\]  
where the overall sample size is \(n\), the value of the probability of \(i\)th the specimen is \"\({P_{i}}^{\infty}\), the parameter of regularization is \"\(\lambda\)\", the dimension of the feature is \"\(p\)\" and the weight of the feature is \"\(w\)\". The weight values for the feature may be very near to \"0\" in case the selection of \"\(\lambda\)\" is performed at random. The relevant features have no importance for the method when weights are very near to zero. Thus, the parameter \(\lambda\) has to be arranged. Iterative Neighborhood Component Analysis (INCA) holds advantages over Neighborhood Component Analysis (NCA) primarily due to its adaptive feature selection mechanism. Unlike NCA, INCA iteratively selects features, allowing it to dynamically adjust and optimize the learning process by choosing the most relevant features for a given task. This adaptability enhances the discriminative power, robustness, and generalization performance. By reassessing and modifying feature selections during each iteration, INCA is better equipped to handle diverse datasets and mitigate the impact of noisy or irrelevant features. The flexibility offered by INCA in tailoring its approach to specific dataset characteristics makes it a promising choice for various machine learning applications. Choosing the parameter \(\lambda\) randomly may not give the best feature selection result. Here, the most reliable way to select the lambda parameter is to use an optimization algorithm. The Stochastic Gradient Descent (SGD) algorithm provides exemplary performance in many optimization problems. In this study, the lambda parameter used in INCA was selected with the SGD optimization algorithm. The pseudocode expression of this algorithm (INCA) is given in Algorithm 1.