14708804
Artificial Ventilation Modeling using Neuro-Fuzzy Hybrid System
Artificial ventilation is a crucial treatment to the patients in Intensive Care Unit. However, as the ventilator increasingly becomes more complex, it is not easy for less experienced clinicians to control the settings. The objective of the paper is to model the FiO2 settings by clinician, using a neuro-fuzzy hybrid system. Two important issues, the interpretability and accuracy are balanced through an iterative reduction and tuning process. Fuzzy sets are merged according to their Hebbian importance, while membership functions are tuned through the Least-Mean-Square (LMS) algorithm. Effective, compact and interpretable fuzzy rules are generated and tested on real ventilation data, benchmarked with other neuro-fuzzy systems.
6728099
A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System
There are two important issues in neuro-fuzzy modeling: (1) interpretabilitythe ability to describe the behavior of the system in an interpretable wayand (2) accuracythe ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.
35820562
Named Entity Recognition in Biomedical Literature: A Comparison of Support Vector Machines and Conditional Random Fields
In this paper, we propose two named entity recognition systems for biomedical literature, System1 using support vector machines and System2 using conditional random fields. Through employing several sets of experiments, we make a comprehensive comparison between these two systems. The final results reflect that System2 can achieve higher accuracy than System1, because System2 can catch more essential properties by handling the richer set of features, i.e., adding not only the individual and dynamic features as System1 does but also the combinational features, which can improve the performance further. Furthermore, with carefully designed features, System2 can recognize named entities in biomedical literature with fairly high accuracy, which can achieve the precision of 89.43%, recall of 83.32% and balanced F β= 1 score of 86.28%.
29468800
SceBoost Learning Algorithm for Feature Selection
This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus foliage. Compared with related feature selection algorithm our method can get improvements in classification accuracy and significantly reduce computation time when reach the same requirements.
10413997
The Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning Algorithm
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects reliability, robustness and accuracy of these algorithms and takes up a large amount of time. To address the above problem, we propose a new Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well-known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency. It is also shown that MRMRG algorithm has better accuracy than most of existing learning algorithms for limited sample datasets.
14466555
Network Optimization based on Genetic Algorithm and Estimation of Distribution Algorithm
Genetic algorithm (GA) is a kind of algorithm that simulates the process and the mechanism of the evolution. Because of its unique biologic feature and its suitability to any function, it becomes very popular and has been used in many problems in many fields. Estimation of distribution algorithm (EDA) is an algorithm that is generated from the GAs. Comparing with GAs, the EDAs replace the crossover and the mutation operations in GAs with learning and sampling the probability distribution of the best individuals of the population at each iteration of the algorithm. Because of its superior, it becomes a hot topic recently. Based on the former researches, this paper mainly focuses on solving the problem of one primary network model named all-terminal network model using the strategies of the evolutionary algorithms.
1617013
Motion Path Synthesis for Intelligent Avatar
In this paper, we present a new motion generation technique, called motion path synthesis. The objective is to generate a motion for an intelligent avatar to move along a planned route using a pre-existing motion library. First, motion primitives, each defined as a dynamic motion segment, are extracted from the motion library. Then a motion graph is constructed based on the motion primitives and their connectivities. Within this motion graph, the desired realistic motion for avatars can be synthesized through a two-stage process: search an optimal motion path within the motion graph, joint the motion path and adapt it to the route. The experiment shows the effectiveness of the presented technique.