Mobile devices that run Android operating system are widely used. The applications running in Android mobiles can have malicious permissions due to malware. In other words, Android applications might spread malware which can sabotage valuable data. Therefore it is essential to have mechanism to classify malware and benign mobile applications running in Android phones. Since Android mobile applications run in the confines of mobile devices and associated servers, it is very challenging task to detect Android malware. Many solutions came into existence to detect malware applications. Of late Abawajy et al. proposed a technique known as Iterative Classifier Fusion System (ICFS) which employs classifiers iteratively with fusion to generate a final classifier for effective detection of malware. They combined NBtree classifier, Multilayer perception and LibSVM with polynomial kernel to achieve this. However, the system does not focus on reduction or pruning of Android application permissions so as to build a classifier that reduces time and space complexity. In the proposed system, a methodology is proposed that focuses on reduction or pruning of Android application permissions and ranking them in order to build a classifier that reduces time and space complexity. The classifier modelled with best ranked permissions can be representative of all permissions as least significant permissions are pruned to reduce search space. We built a prototype application to demonstrate proof of the concept. The experimental results revealed that the proposed system performs better in improving detection accuracy besides precision and recall measures.