# Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances
## 4 Results
### State of the Art Classifiers for Comparison  
This subsection contains brief descriptions of five of the most important classifiers of the state of the art, which were implemented on the WEKA platform [31]. These are the classification algorithms against which our proposal, the D1-NN classifier, will be compared.  
**Naive Bayes**  
The Naive Bayes classifier [36] is a probabilistic algorithm with a superstructure based on [PERSON] theorem. The classifier assumes that the features are independent of each other, and that is why it contains the word \"naive\" in its name.  
**Nearest Neighbor (kNN)**  
This is one of the first supervised classifiers [14], and despite its simplicity, has very good performance. Nearest neighbor is an instance-based classifier, which uses dissimilarity or distance functions to select the closest instance to the pattern to classify.  
**Logistic Regression (Logistic)**  
Logistic regression is a statistical learning technique [31], used for both regression and classification problems. It has been widely used, and has a small computational complexity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Number of** \\ **Patterns** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} & \
\begin{tabular}{c} **IR** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Classes** \\ \end{tabular} \\ \hline Australian credit approval & 690 & 14 & 1.24 & 2 \\ Bank & 4521 & 16 & 7.67 & 2 \\ Bank additional & 4119 & 20 & 8.13 & 2 \\ Banknote authentication & 1372 & 4 & 1.24 & 2 \\ Credit Approval & 690 & 15 & 1.24 & 2 \\ German credit data & 1000 & 24 & 2.33 & 2 \\ Iranian credit & 1583 & 28 & 1.50 & 2 \\ Qualitative bankruptcy & 250 & 6 & 1.33 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the datasets (in alphabetical order).  
**Multi-Layer Perceptron (MLP)**  
The multi-layer perceptron with backpropagation classifier [37] is an artificial neural network consisting of multiple layers, which allows solving non-linear problems. Although neural networks have many advantages, they also have their limitations. If the model is not trained correctly, it can give inaccurate results, in addition to the fact that the functions only look for local minima, which causes the training to stop even without having reached the percentage of allowed error.  
**Support Vector Machines (SVM)**  
The optimization of analytical functions serves as a theoretical basis in the design and operation of SVM models, which attempts to find the maximum margin hyperplane to separate the classes in the attribute space. SVMs [38] continue to occupy the first places in performance in classification problems. The relevance of SVMs has been emphasized as one of the most appreciated classifiers by the international scientific community. For this reason, we have included a representative model of the SVM in the experimental section of this paper.  
**Adaptive Boosting (AdaBoost)**  
Ensemble classifiers are methods which aggregate the predictions of a number of diverse base classifiers in order to produce a more accurate predictor, with the idea being that \"many heads think better than one\". Ensemble models are valuable tools in pattern classification, routinely achieving excellent results on complex tasks. In this paper, we have selected a boosting ensemble, the AdaBoost algorithm [39].  
Table 2 contains a summary of the specifications for the five algorithms.