Attribute reduction from decision tables is one of the crucial topics in data mining. This problem belongs to NP-hard and many approximation algorithms based on the filter or the filter-wrapper approaches have been designed to find the reducts. Intuitionistic fuzzy set (IFS) has been regarded as the effective tool to deal with such the problem by adding two degrees, namely the membership and non-membership for each data element. The separation of attributes in the view of two counterparts as in the IFS set would increase the quality of classification and reduce the reducts. From this motivation, this paper proposes a new filter-wrapper algorithm based on the IFS for attribute reduction from decision tables. The contributions include a new instituitionistics fuzzy distance between partitions accompanied with theoretical analysis. The filter-wrapper algorithm is designed based on that distance with the new stopping condition based on the concept of delta-equality. Experiments are conducted on the benchmark UCI machine learning repository datasets.

Random pairwise key pre-distribution schemes have been adopted extensively as a preferred approach to tackling the pairwise key agreement problem in wireless sensor networks (WSNs). However, their practical applicability is threatened by the key-swapping collusion attack (KSCA) whose goal is to ruin critical applications that requires collaborative efforts of sensor nodes such as data aggregation mechanisms, routing protocols, distributed voting schemes and misbehaviour detection systems, etc. In this paper, we propose a light-weight framework for thwarting the attack. Our proposed framework makes good use of a winning combination of incremental sensor node deployment and a diversified one-way hash chain. The framework thereby evades undesirable costly requirements of additional functionalities and resources to aggregators and base stations, topological knowledge in advance or costly location-based detection algorithms, yet maintaining network scalability. Moreover, the in-depth analytical and experimental analyses conducted on two node capture attack models show that the framework eradicates the KSCA under one attack while demonstrating most likely immunity under the other attack. Finally, the detailed performance evaluation carried out via simulations indicates the plausibility of the framework for use in the current generation of sensor nodes.This framework is applicable to any random pairwise key pre-distribution scheme. However, for ease of presentation and demonstration, we only consider Chan et al. random pairwise keys scheme [3] as a particular example. The main contributions of this paper are as follows: