# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 2 Related Works
### Feature Selection Techniques
Since our purpose in this paper is to demonstrate the value of feature selection for insurance domain and not to assess state of art feature selection techniques, we will only briefly outline these developments. The first [PERSON] et al. paper [43] developed a filter feature selection algorithm based on Ant Colony Optimisation (ACO). Their approach specially designed for multi-labelled data uses artificial agents called ants whose behaviour is modelled on real ants. As the ants traverse the feature space they leave a pheromone trail which is updated in each iteration. At the end of all iterations the top \(k\) features with the highest pheromone intensity are selected. The search for the most informative features is modelled as a graph with nodes representing features. In contrast to previous ACO approaches where the initial pheromone value for each node is a constant, the authors here initialise the pheromone values by calculating the normalized cosine similarity between each feature and class label. Ants are randomly placed on nodes and the next step of the ants is decided by a decision policy. Interestingly, the heuristics involved in the decision making take account of both redundancy, the correlation between the features, and relevancy, the correlation between features and class labels. The pheromone values of each node are then updated and the process is repeated until the maximum number of iterations is reached. Recent ensemble-based feature selection methods are proposed: Ensemble of Feature Selection Multi-Criteria Decision-Making (EFS-MCMD) algorithm [44] and Pareto-based Ensemble of Feature Selection (PEFS) algorithm [45]  
In EFS-MCMD, the authors combine the understanding of how it may be possible to improve the robustness of feature selection methods through using more than one algorithm with the idea that using the Multi-Criteria Decision-Making (MCDM) process may get the best results from the ensemble model. Ensemble methods aim to overcome the problem of a single feature selection algorithm finding only a local optima of selected features from the whole feature space. Combining the output of multiple feature selection algorithms can overcome this problem and improve accuracy. Ensemble feature selection methods can be divided into two categories: homogeneous and heterogeneous. Homogeneous approaches partition the data into subsets and apply a single feature selection algorithm to each subset and then combine the ranking from each subset. Heterogeneous approaches apply multiple feature selection algorithms to the whole dataset and combine the results of each algorithm into a final ranking. The authors use a heterogeneous approach. EFS-MCDMs are methods where multiple and sometimes conflicting opinions and criteria are weighed to reach the best alternatives and are particularly useful for solving problems where ranking is required. The authors use the VIKOR (from Serbian: Vlsekriterijumska Optimizacija I Kompromisno Resenje) method for solving the MCDM problem which involves constructing a decision matrix. This implementation of the VIKOR method takes the proposed top k most relevant features of each feature selection algorithm as criteria for the decision matrix. The VIKOR method ranks the features by the VIKOR index value. The method proceeds in the following way. The user selects the number of features required, the ensemble feature selection algorithm is run, and then the VIKOR method is applied to find the most relevant features. The results based on experiments with real-world datasets are impressive in terms of accuracy, f-score, and run-time. Another ensemble feature selection algorithm is PEFS [45]. It employs multiple filter based feature selection algorithms and multi-objective optimization techniques to obtain the final optimal feature subset (dominant solution). If there is no single optimal solution, the feature selection algorithms in PEFS deliver a set of trade-off solutions called the Pareto optimal set. These solutions are called non-dominated solutions.