# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 3 LocFiND--Localization Using Fingerprinting and Novelty Detection
### Using Novelty Detection  
In the previous solution, as we will show in Section 4, even when discarding classifications that have a low prediction probability, we still have some wrong classifications. In this section, we define a novel approach that helps to decide which classifications should be discarded. In this solution, which we refer simply as Novelty Detection, we check (i.e., confirm) whether the classified sample really belongs to the class that was estimated by the classifier (KNN chooses the class that most closely matches). For this, we use the concept of novelty, a concept used in the area of outlier detection. Novelty can be defined as the task of recognizing whether a tested sample differs, in some aspects, from the data gathered during the training phase [45].  
Detecting new, never seen samples is an important skill for any classification system. When realizing that it is not possible to train a machine learning system with all types of data the system is likely to find, it becomes important to be able to differentiate between information from known and unknown data during the online phase [46].  
Novelty detection refers to the identification of novel or abnormal patterns embedded in a large amount of normal data. Novelty (anomaly, outlier, exception) happens when a sample does not conform to the expected, normal behavior [47]. Unlike outlier detection approaches, that are done in training, novelty detection defines whether or not a new sample (online phase) belongs to a class. A novelty detection architecture is depicted in Figure 3.  
Novelty detection techniques can be implemented using different techniques such as support vector machines based techniques, clustering-based techniques, nearest-neighbor-based techniques, and statistical techniques [48].  
A commonly used unsupervised novelty detection technique is based on SVM. It builds a spherical boundary scheme that groups and encloses most normal data samples. Any new, unseen sample falling outside the group's boundaries is detected as novelty. In recent works on SVM-based novelty detection, a variation called OC-SVM (one-class SVM) is used since it has some kernel tricks that allow it to easily separate normal and novel data in higher dimensional spaces [49], which is the case of our localization system with 15 dimensions (i.e., anchor nodes).  
In this evaluation of confidence part of LocFiND, we first use the prediction probability, as in the previous Section 3.1, to have a first evaluation from the KNN itself regarding the  
Figure 3: Novelty detection using One-Class SVM.  
Figure 2: Evaluation of confidence using prediction probability: classifications with low probabilities are discarded to improve accuracy.  
classification reliability. Then, in order to have a second validation of the result, we use the concept of novelty detection to confirm that a given sample really belongs to that class.  
Thus, the same prediction probability explained previously with the threshold \(\alpha\) is used. However, the classifications are further validated using novelty detection implemented using the One-Class SVM. In addition to passing the threshold test, a new validation is done by the novelty algorithm that tests whether that sample really belongs to the chosen class (the one with the highest probability returned by KNN). Only when both tests are passed, a classification is accepted. This complete decision process for the evaluation of confidence is depicted in Figure 4.