## 3 The Proposed Model
### Process Involved in GEO-FS Technique  
In this study, the GEO-FS technique is exploited to choose an optimal subset of features from the preprocessed data. GEO is a novel meta-heuristic approach, which was established very recently for solving global optimized problems. The GEO technique was simulated and mathematically processed by the intelligence of golden eagle (GE) dependent upon adjusting the speed of its spiral tracking [21]. The GE is a specific type of swarm that has a superior propensity for cruising around and searching for prey initially to hunt. By controlling these two elements, i.e., cruise propensity and attack propensity, the GEO was rapidly capable of hunting an optimum accessible prey from the possible region. The GE from cruise and hunt is a unique feature, i.e., it follows in a spiral trajectory, representing that prey was commonly on one side of the eagles. This allows them to control target prey wisely and use boulders for determining an appropriate angle of attack. Simultaneously, it can be checking other regions for optimum food. The hunting technique of GEs mostly dependent upon the subsequent feature. The mathematical designs of GEs for mimicking the movement to search for the prey are mostly explained as:  
* The spiral movement of GEs: In GEO, all the GE retains from their memory the optimum visited place previously. The eagle is an attraction near the cruising and nearing attack of the prey concurrently in order to search for optimum food. At all iterations, all the GEs \(j\) arbitrarily select prey, which is fixed by another GE \(l\), and circles around optimum place stayed by GE \(l\) so far. The GE \(j\) also has the chosen features for circling their memory; therefore, it can be \(l\in\{1,2,\ \cdots,N_{GE}\}\), whereas \(N_{GE}\) stands for the number of GEs. * Prey selection: At all the iterations, all the GEs can choose a prey for executing the cruising as well as attacking functions. Additionally, all the GEs select the chosen prey in the memory of the entire flock. Thus, the cruising and attacking vectors are calculated based on the chosen prey. Next, it verifies their memory when the novel place was superior to the preceding place. Subsequently, the memory was upgraded with novel determining. * Attack: The attack is defined by utilizing a vector deriving from the actual place of GE \(j\) and ending from the place of prey from the eagle's memory as: \[\overset{\rightarrow}{A_{j}}=\overset{\rightarrow}{X_{l}^{*}}-\overset{ \rightarrow}{X_{j}},\] (2) where \(\overset{\rightarrow}{A_{j}}\), implies the attack vector of GEs \(j\), \(\overset{\rightarrow}{X_{1}^{*}}\) denotes the optimum place visited by eagle \(l\) so far, and \(X_{j}\) signifies the present place of eagles \(j\). * Cruise: The cruise vector is a perpendicular vector to attack vectors and tangent to circle. It is also well-known as linear speed of GEs for attacking the prey. The target point on cruise vector was provided under: \[\overset{\rightarrow}{C_{j}}=\frac{d-\sum_{f,f\
eq j}a_{f}}{a_{f}^{\prime}},\] (3) where \(d\) signifies the hyperplane formula from \(n\)-dimension space, \(a_{j},a_{f}\in\overset{\rightarrow}{A_{j}}\), whereas \(\overset{\rightarrow}{A_{j}}=[a_{1},a_{2},\cdots,a_{n}]\) signifies the attack vectors. * Moving to novel places: Moving to novel places of GEs was mostly dependent upon the attacking and cruising vectors. Thus, the step vector of GEs \(j\) in iteration \(t\) was projected by the subsequent formula: \[\triangle_{x_{j}}=\overset{\rightarrow}{r_{1}}p_{a}\frac{\overset{ \rightarrow}{A_{j}}}{\|\overset{\rightarrow}{A_{j}}\|}+\overset{ \rightarrow}{r_{2}}p_{c}\frac{\overset{\rightarrow}{C_{j}}}{\|\overset{ \rightarrow}{c_{j}}\|}\] (4) In which \(p_{a}^{t}\) denotes the attack co-efficient at iteration \(t\) and \(p_{c}^{t}\) implies the cruise co-efficient at iteration \(t\) and controlling that the GE was affected by cruising and attacking. \(\overset{\rightarrow}{r_{1}}\) and \(\overset{\rightarrow}{r_{2}}\) imply the random vectors.