## 3 Method
### Method Detailed Description
#### 3.2.3 First Instance Classification  
In this stage, GADHA counts with ensemble techniques and with the physical model to predict the state-condition of the system. It is necessary to thoroughly estimate the initial state parameters of the physical model once the behavior of the system is an outgrowth of them. There are scenarios where the position measurement is straightforward, and the estimation of those initial values becomes a noise reduction issue. However, there is one scenario that only provides accelerometer and gyroscope measurements. In this case, the estimation becomes more laborious and requires more steps. Thus, it will demonstrate the data-driven methods responsible for estimating the release height, when just acceleration is available. When model-based pre-processing techniques are used to reduce input data dimensionality, it is done based on specialized knowledge about the system design or behavior, and it is also called model-based feature extraction. This technical knowledge is expressed either by a set of rules to determine if a particular feature is present or not or by the definition of acceptable thresholds based on system noise modeling. When model-based pre-processing techniques are used to filter input data, on a data cleaning approach, it is done by implementing filters based on the system noise and expected behavioral characteristics. Activities 2 and 3 are not mutually exclusive and can be performed in conjunction to reduce input data dimensionality (features extraction) or to filter input data. This means that if one wishes to create an embracing number of extracted features, he may consists ofder features extracted by either data-driven pre-processing or by model-based pre-processing. Furthemore, the data cleaning approach may encompass both data-driven filters as well as model-based filters. Ensemble Learning Classification (Data-Driven)  
The ensemble learning classification performed on Activity 4 of GADHA receives as inputs all features extracted from Activities 2 and 3, and classify system state, estimating what is the most probable state among a finite and enumerable list of possible system states. To perform this classification, initially, ensembles shall be created considering sets of machine learning inducers. Each ensemble groups a certain number of inducers, and each inducer attempts to estimate system state based on input data. The final classification of each ensemble corresponds to the best estimate considering the ensemble inducers estimates as well as the performance metrics of each inducer. In the scope of this work, the final classification of each ensemble is called the committee classification technique. Inducers or learners are machine learning algorithms in the scope of this work. They are split into two categories: parametric and non-parametric, depending if the algorithm is parametric or not. Support Vector Machine (SVM) and Decision-Tree (DT) are examples of machine learning non-parametric algorithms, while Artificial Neural Network (ANN) and Naive Bayes (NB) are examples of parametric inducers. At GADHA, it is proposed the usage of ensembles composed of three inducers for simplicity because even values are more prone to draws, and three is the smallest non-unit positive odd value of ensemble inducers quantity. These three inducers ensembles are called Ensemble Triads (ET). Based on the above definitions, Ensemble Triads can be classified as Heterogeneous ET, Homogeneous non-parametric ET, or Homogeneous parametric ET. Figure 3 shows the ET general concept.