# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Document Classification
The retained concepts are assigned a _TFIDF_ weight to form the feature vector matrix that will be used in the classification. In the second stage, the particle swarm optimization [65] algorithm is used to select the optimal feature subset. The particles are initialized randomly by numbers in \([-1,1]\), where a positive number indicates an active feature while a negative value an inactive one. The fitness function for each particle is based on the classification accuracy,  
\[Fitness(S)=\frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
where \(S\) represents the features set, \(TP\) (True Positive) and \(FP\) (False Positive) are the number of correctly and incorrectly identified documents and \(TN\) (True Negative) and \(FN\) (False Negative) the number of correctly and incorrectly rejected documents. The particle's fitness value is estimated as the average of the accuracies using a 10-fold cross validation procedure. The authors evaluated their method using five classifiers (NB, LSVM, KNN, DT, LR) and reported both significant reduction of the feature space and improved accuracy of the classification in most of their tests. [PERSON] et al. [66] attempt to predict the probability of hospital readmission within 30 days after a heart failure, by means of the medication list prescribed to patients during their initial hospitalization. In the first stage, the authors combine two publicly accessible drug ontologies, namely RxNorm [67] and NDF-RT [68], into a tree structure, that represents the hierarchical relationship between drugs. The RxNorm ontology serves as drug thesaurus, while NDF-RT as drug functionality knowledge base. The combined hierarchy consists of six class levels. The top three levels correspond to classes derived from the Legacy VA class list in NDF-RT and represent the therapeutic intention of drugs. The fourth level represents the general active ingredients of drugs. The fifth level refers to the dosage of drugs and uses a unique identifier to match drugs to the most representative class in RxNorm (RXCUI). The lowest level refers to the dose form of drugs and uses the local drug code used by different hospitals. Each clinical drug corresponds to a single VA class, a single group of ingredients, and a single RxNorm class. In the second stage, a top-down depth-first traversal of the tree hierarchy is used to select a subset of nodes as features. For each branch, the nodes are sorted according to the information gain ratio (IGR(F) = IG(F)/H(F)). The features in the ordered list are marked for selection one by one, while parent and child features with lower scores are removed from the list. In order to evaluate their method, the authors use the naive Bayes classifier and employ the area under the receiver operating characteristic curve to evaluate its performance. Their experiments showed that the ontology-guided feature selection outperformed the other non-ontology-based methods. [PERSON] et al. [69] explore the use of the semantic web and domain ontologies to automatically detect indicators and warning signals, emitted from messages and posts in social networks, during the radicalization process of vulnerable individuals and their recruitment from terrorist organizations. Specifically they devise an ontology for the radicalization domain and exploit it to automatically annotate social messages (tweets). These annotations are combined with a reasoning mechanism to infer values of pre-determined radicalization indicators according to a set of inference rules. The ontology is built in two steps. First, a group of experts define and organize the main classes, properties and relationships. These are related to high level concepts, which represent the radicalization indicators, namely \"Perception of discrimination for being Muslim\", \"Expressing negative ideas about Western society\", \"Expressing positive ideas about jihadism\", \"The individual is frustrated\" and \"The individual is introvert\".