In Multiple Instance Learning (MIL) problem for sequence data, the learning data consist of a set of bags where each bag contains a set of instances/sequences. In many real world applications such as bioinformatics, web mining, and text mining, comparing a random couple of sequences makes no sense. In fact, each instance of each bag may have structural and/or temporal relation with other instances in other bags. Thus, the classification task should take into account the relation between semantically related instances across bags. In this paper, we present two novel MIL approaches for sequence data classification: (1) ABClass and (2) ABSim.

We applied both approaches to the problem of bacterial Ionizing Radiation Resistance (IRR) prediction. We evaluated and discussed the proposed approaches on well known Ionizing Radiation Resistance Bacteria (IRRB) and Ionizing Radiation Sensitive Bacteria (IRSB) represented by primary structure of basal DNA repair proteins. The experimental results show that both ABClass and ABSim approaches are efficient.

Computations were carried out on a i7 CPU 2.49 GHz PC with 6 GB memory, operating on Linux Ubuntu. In the classification process, we used the Leave-One-Out (LOO) technique.

Both ABClass and ABSim approaches provide good overall accuracy results since the least accuracy percentage is 89.2%. This clearly shows that our proposed approaches are efficient. Using ABSim approach with the SMS aggregation method provides a better accuracy result compared to the WAMS aggregation method. The best result was reached using ABClass approach, J48 classifier and the motif extraction settings 3 and 4. Using these two settings, a large number of non discriminative motifs are extracted.

Fig 2. Accuracy percentage using the naive approach, ABClass approach and ABSim approach.