Hatem M. Abdul-Kader

A novel artificial immune clonal selection classification and rule mining with swarm learning model

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Semantic anonymization in publishing categorical sensitive attributes

The need of improving the privacy on data publisher becomes more important because data grows very fast. Traditional methods for privacy preserving data publishing cannot prevent privacy leakage. This causes the continuous research to find better methods to prevent privacy leakage. K-anonymity and L-diversity are well-known techniques for data privacy preserving. These techniques cannot prevent the similarity attack on the data privacy because they did not take into consider the semantic relation between the sensitive attributes of the categorical data. In this paper, we proposed an approach to categorical data preservation based on Domain-based of semantic rules to overcome the similarity attacks. The experimental results of the proposal approach focused to categorical data presented. The results showed that the semantic anonymization increases the privacy level with effect data utility.

Automatic Rules Generation Approach for Data Cleaning in Medical Applications

Data quality is considered crucial challenge in emerging big data scenarios. Data mining techniques can be reutilized efficiently in data cleaning process. Recent studies have shown that databases are often suffered from inconsistent data issues, which ought to be resolved in the cleaning process. In this paper, we introduce an automated approach for dependably generating rules from databases themselves, in order to detect data inconsistency problems from large databases. The proposed approach employs confidence and lift measures with integrity constraints, in order to guarantee that generated rules are minimal, non-redundant and precise. The proposed approach is validated against several datasets from healthcare domain. We experimentally demonstrate that our approach outperform significant enhancement over existing approaches.