# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 2 Materials and Methods
### Classifiers  
The classifiers used in this study are SMO from Function Classifiers, IBk from Lazy Classifiers, Random Forest and Optimized Forest from the Tree Structures, FURIA (Fuzzy Unordered Rule Induction Algorithm) from Rules, Bagging, Random Committee, and Random Subspace from Meta Classifiers. Classifiers are used with default parameters of the WEKA program, except for IBk. A 10-fold cross-validation is applied to the data during classifications.  
IBk (Instance-Based Learning): the K-Nearest Neighbor (KNN) algorithm (lazy learning) is a supervised learning method used in a wide variety of fields in machine learning areas, such as pattern recognition, signal, image processing, etc. [25]. IBk is a KNN classifier. Several nearest neighbors can be explicitly specified or automatically determined. Different search algorithms can be used to speed up the finding process of nearest neighbors. IBk uses the same distance metric as KNN, and the distance function is a search method parameter [40]. The KNN classifier uses distances, such as Euclidean, Manhattan, Chebychev, Minkowski, etc., to determine the nearest neighbor in feature space [25]. This study uses Manhattan distance and 1 nearest neighbor parameters (K = 1) for classification.  
SMO (Sequential Minimum Optimization): SVM is another supervised learning method [25], and an SVM quadratic programming problem can be easily solved with SMO. It is a simple algorithm for solving the problem by separating it into subproblems that are solved extremely fast, causing the overall problem to be solved too rapidly [41].  
It is possible to achieve accurate classification and regression with suitable randomness. Random Forest (RF) is developed by [PERSON], and it is an effective tool in prediction [42]. An ensemble classification method with high accuracy results, it randomly builds multiple trees in subspaces of the feature space [43]. The general error depends on the strength and correlation of the individual trees in the forest [42].  
FURIA (Fuzzy Unordered Rule Induction Algorithm) is an advanced version of the RIPPER algorithm, which is a rule learner after some modifications and extensions. For instance, it uses fuzzy rules instead of conventional rules, it uses unordered rule sets instead of rule lists, and it uses a novel rule stretching technique. According to some experiments, FURIA outperforms the original RIPPER algorithm and some other fuzzy rule learning methods [44].  
The Bagging method generates predictor versions by making bootstrap replicates of the learning set, and then it uses them to obtain an aggregated predictor. Classification and regression experiments with real and simulated datasets show that Bagging can significantly improve accuracy [45].  
Random Committee (RC) is a classification method that builds an ensemble of randomizable base classifiers. Different random number seeds are used for each base classification, and predictions from each are averages to form the overall prediction value [21].  
Random Subspace (RSS) is formed by multiple trees such that subsets will be selected pseudo-randomly from the feature vector. It forms a decision-tree-based classifier with the highest training data accuracy [21].  
The Optimized Forest (OF) algorithm determines the number of trees for maintaining an optimal sub-forest using a Genetic-Algorithm-based technique. The method selects a small sub-forest from a large forest. An optimum number of diverse and accurate trees could improve the absolute accuracy besides its computational advantage, as many trees could be less valuable in a forest [46].