Active Sample Selection Based Incremental Algorithm for Attribute Reduction with Rough Sets

Attribute reduction with rough sets is an effective technique for obtaining a compact and informative attribute set from a given dataset. However, traditional algorithms have no explicit provision for handling dynamic datasets where data present themselves in successive samples. Incremental algorithms for attribute reduction with rough sets have been recently introduced to handle dynamic datasets with large samples, though they have high complexity in time and space. To address the time/space complexity issue of the algorithms, this paper presents a novel incremental algorithm for attribute reduction with rough sets based on the adoption of an active sample selection process and an insight into the attribute reduction process. This algorithm first decides whether each incoming sample is useful with respect to the current dataset by the active sample selection process. A useless sample is discarded while a useful sample is selected to update a reduct. At the arrival of a useful sample, the attribute reduction process is then employed to guide how to add and/or delete attributes in the current reduct. The two processes thus constitute the theoretical framework of our algorithm. The proposed algorithm is finally experimentally shown to be efficient in time and space.This is a manuscript of the publication Yang, Yanyan, Degang Chen, and Hui Wang. "Active Sample Selection Based Incremental Algorithm for Attribute Reduction With Rough Sets." IEEE Transactions on Fuzzy Systems 25, no. 4 (2017): 825-838. DOI: 10.1109/TFUZZ.2016.2581186. Posted with permission.</p

Plantar pressure repeatability data analysis for healthy adult based on EMED system

This paper presents the repeatability data analyses and discusses the selection of the appropriate type of plantar pressure measurements for the EMED system with regards to Pressure Level Values (PLV) over the touch insole area of healthy adults. In this research, a participant with age 28 years old has been chosen as a sample to measure under foot pressure, it is conducted the test 20 times and took part in four types of plantar pressure clinical assessments, Dynamic (normal walking), Dynamic with load (normal walking, carrying 1.5 Kg), Static (Standing test), and Static with load (Standing, carrying 1.5 Kg). The analysis is implemented using a new approach of recognizing the measurements into 7 different levels of pressure that assigned with 7 colors by considering the image processing algorithm. Variance Coefficient (VC) check is adopted for the statistical analysis and the selection decision. The results highlighted that the overall pressure levels in dynamic with load category have a better variance as compared with three other categories of plantar pressure on this type of repeatability test. In conclusion, EMED system can be considered as an effective instrument to record plantar foot pressure measurements in such type of analysis

A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing with Diverse Sensing Costs

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely cells), Sparse Mobile Crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current Sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in real-world, as the cost of sensing in a subarea can be diverse due to many factors, e.g. condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable bi-objective optimization problem under certain assumptions and approximation. Then, we presented two selection strategies, i.e. Pareto Optimization Selection (POS) and Generalized Cost-Benefit Greedy (GCB-GREEDY) Selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring datasets (Parking, Flow, Traffic, and Humidity) and three cost maps (i.i.d with dynamic cost map, monotonic with dynamic cost map and spatial correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-Query by Committee (QBC) in a sensing cycle. The findings show important implications in Sparse Mobile Crowdsensing for urban context properties