Selection of a representative set of features is still a crucial and challenging problem in machine learning. The complexity of the problem increases when any of the following situations occur: a very large number of attributes (large dimensionality); a very small number of instances or time points (small-instance set). The first situation poses problems for machine learning algorithm as the search space for selecting a combination of relevant features becomes impossible to explore in a reasonable time and with reasonable computational resources. The second aspect poses the problem of having insufficient data to learn from (insufficient examples). In this work, we approach both these issues at the same time. The methods we proposed are heuristics inspired by nature (in particular, by biology). We propose a hybrid of two methods which has the advantage of providing a good learning from fewer examples and a fair selection of features from a really large set, all these while ensuring a high standard classification accuracy of the data. The methods used are antlion optimization (ALO), grey wolf optimization (GWO), and a combination of the two (ALO-GWO). We test their performance on datasets having almost 50,000 features and less than 200 instances. The results look promising while compared with other methods such as genetic algorithms (GA) and particle swarm optimization (PSO). (H.M. Zawbaa). experiment with an animal over the duration of a number of years) [2]. This determines some datasets to have fewer instances. We refer to this aspect as a small-instance set. In the field of machine learning, feature selection is one of the most studied problems. There exist a large amount of work dealing either with high-dimensional feature selection problems or with smallinstance set problems. It is far more complex and challenging to deal with both these aspects at the same time and this for two reasons: the search space becomes too large to allow exploration of each state and the small number of instances do not provide sufficient examples to learn from Ref. [3]. Evolutionary computing (EC) and population-based algorithms adaptively search the feature space by using a set of search agents that interact in a social manner to reach the optimal solution [4, 5] . EC methods are inspired by the animal social and biological behavior in nature like wolves, antlions, dragonflies, spiders, and so on, in a group [6] .
Zawabaa et al. REF have presented a hybrid bioinspired heuristic algorithm which combines Ant-Lion Optimization (ALO) and Grey Wolf Optimization (GWO) algorithms for feature selection.
Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach
Swarm Evol. Comput.
Swarm Evol. Comput.
Computer Science