Feature Selection with a Local Search Strategy Based on the Forest Optimization Algorithm

Feature selection has been widely used in data mining and machine learning.Its objective is to select a minimal subset of features according to some reasonable criteria so as to solve the original task more quickly.In this article,a feature selection algorithm with local search strategy based on the forest optimization algorithm,namely FSLSFOA,is proposed.The novel local search strategy in local seeding process guarantees the quality of the feature subset in the forest.Next,the fitness function is improved,which not only considers the classification accuracy,but also considers the size of the feature subset.To avoid falling into local optimum,a novel global seeding method is attempted,which selects trees on the bottom of candidate set and gives the algorithm more diversities.Finally,FSLSFOA is compared with four feature selection methods to verify its effectiveness.Most of the results are superior to these comparative methods.

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physicians’diagnostic processes in terms of speed and accuracy.Using data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms.In this work,we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’information and exposing them to cyber and network attacks.Solving the same problem with a different methodology,the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms.Moreover,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.

Access Control Policy Based on Friend Circle

Nowadays,the scale of the user’s personal social network(personal network,a network of the user and their friends,where the user we call“center user”)is becoming larger and more complex.It is difficult to find a suitable way to manage them automatically.In order to solve this problem,we propose an access control model for social network to protect the privacy of the central users,which achieves the access control accurately and automatically.Based on the hybrid friend circle detection algorithm,we consider the aspects of direct judgment,indirect trust judgment and malicious users,a set of multi-angle control method which could be adapted to the social network environment is proposed.Finally,we propose the solution to the possible conflict of rights in the right control,and assign the rights reasonably in the case of guaranteeing the privacy of the users.

FMAC: Fair Mac Protocol for Achieving Proportional Fairness in Multi-Rate WSNs

In a multi-rate wireless environment, slow nodes occupy the channel for longer time than fast nodes and thus the total throughput of the network will be reduced. In this research, we study the problem of fairness in multi-rate wireless sensor networks. To improve the fairness, we propose a new protocol, FMAC (Fair MAC protocol) that is based on IEEE 802.11 MAC protocol to achieve proportional fairness between all nodes. FMAC protocol includes medium delay periods within Backoff algorithm to utilize the idle slots of time and reduce the number of collisions and then number of retransmissions, and thus reducing the energy consumption, which is very critical in wireless sensor networks. The experimental results show that transmissions become faster with less collisions and power consumption when applying FMAC, while the aggregated throughput and proportional fairness are increased. The detailed performance evaluation and comparisons are provided using the simulation.

Enhancing Performance of Multi-Rate WLANs: Ant Colony Approach

The trade-off between users’ fairness and network throughput may be unacceptable in a multi-rate 802.11 WLAN environment. In this paper, we will design a new intuitive simplified mathematical model called simplified coefficient of variation (SCV) to closely reflect our topic. Through controlling the power of Access Points, SCV can optimize and improve the performance. Since our topic is a NP-hard problem, we use Ant Colony Algorithm to solve our model in a practical scenario. The simulation shows excellent results indicating that our model is efficient and superior to an existing method. Also we use software SAS to further reveal the relationships among the three indicators to illustrate the essence of our approach and an existing algorithm.