## 3 Methodology
### Classification  
Using all extracted features which may contain noisy and irrelevant features as input parameters may cause a classifier to have poor generalization capability and require intensive computation time. In bioinformatics applications, there are two ways to improve classification accuracy. They are: selecting the significant features and choosing the best-suited classifier. In this study, we handled these two issues using a double-strategy random forest algorithm. The reason for utilizing random forest is that it provides favorable results for unbalanced data classification, and is more robust in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. ** & **Feature Name** & **Description** \\ \hline
1. & Area (A) & It is represented as the actual number of pixels inside the nucleus region. \\ \hline
2. & Perimeter (P) & This is measured by computing the total number of pixels on the nucleus edge. \\ \hline
3. & Roundness & This is defined by \(\frac{4\pi\times\pi\text{ }\text{ }erg}{printed}\), which represents the similarity between the nucleus region and a circle. It varies between 0 and 1 and a circle’s roundness circularity is equal to 1. \\ \hline
4. & Solidity & This specifies the proportion of the pixels in the convex hull that is also in the nucleus region. It is formulated as: \(\frac{Area}{\text{ }CACArea}\). \\ \hline
5. & Equivalent Circular & This is defined as the diameter of a circle with the same area as the nucleus region. It is represented using; \(\sqrt{\frac{4\times\pi\text{ }\text{ }Amp}{p}}\). \\ \hline
6. & Compactness & This specifies the ratio of area and square of the perimeter. It is computed as \(\frac{Area}{perimately}\). \\ \hline
7. & Eccentricity & This represents the eccentricity of the ellipse that has the same second-moments as the nucleus region. Its value is between 0 and 1. A cell whose eccentricity is 0 is a circle, while 1 is a line segment. \\ \hline
8. & Local minima & This represents the number of local minimum points in the nucleus region. \\ \hline
9. & Aspect ratio of the nucleus: & This is represented by the ratio of nucleus width to nucleus height using: \(\frac{Width_{Position}}{Height_{Position}}\). \\ \hline
10. & Major Axis & This represents the length (in pixels) of the major axis of the ellipse that has the same normalized second central moments as the nucleus region. \\ \hline
11. & Minor Axis & This specifies the length (in pixels) of the minor axis of the ellipse that has the same normalized second central moments as the nucleus region. \\ \hline
12. & Elongation & This is represented by the ratio between the major and minor axis using: \(\frac{materials}{materials}\). \\ \hline
13. & Actual Diameter (AD) & This is represented by the circle’s diameter circumscribing the nucleus region. It is formulated as; \(\frac{perimately}{2\times\pi\text{dealing with noisy data. Our dataset of nuclei was highly unbalanced, with the number of overlapping nuclei constituting only a very small minority of the dataset at 625 (16%) and single nuclei constituting an abundant majority of the dataset at 3275 (84%). Random forest (RF) is one of the most successful ensemble classification models which was proposed by [PERSON] [29; 30], and later by [PERSON] [31]. RF is an ensemble of decision trees which integrates the idea of [PERSON]'s \"bagging (bootstrap aggregation)\" and [PERSON]'s \"random variable selection\". The principle of RF is to build multiple decision trees using randomized bootstrapped samples from a learning dataset and randomly selecting a subset for training data. Each decision tree, also known as a Classification And Regression Tree (CART), is grown using randomized bootstrap samples of input data and generates its own classification results. RF finally aggregates the predictions of all decision trees by majority voting. The block diagram of RF is depicted in Figure 11. Observations that are not contained in training bootstrap samples are \"out-of-bag\" (OOB), and they are used for predicting errors.