Jiming Liu

Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

BackgroundIn systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials.MethodsIn this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials.ResultsWe demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications.ConclusionsIn this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations.

Community Mining from Signed Social Networks

Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past; however, most of them were designed primarily for networks containing only positive relations and, thus, are not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks where both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making it effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly generated signed networks.

Multi-agent oriented constraint satisfaction

This paper presents a multi-agent oriented method for solving CSPs (Constraint Satisfaction Problems). In this method, distributed agents represent variables and a two-dimensional grid-like environment in which the agents inhabit corresponds to the domains of the variables. Thus, the positions of the agents in such an environment constitute the solution to a CSP. In order to reach a solution state, the agents will rely on predefined local reactive behaviors; namely, better-move, least-move, and random-move. While presenting the formalisms and algorithm, we will analyze the correctness and complexity of the algorithm, and demonstrate the proposed method with two benchmark CSPs, i.e., n-queen problems and coloring problems. In order to further determine the effectiveness of different reactive behaviors, we will examine the performance of this method in deriving solutions based on behavior prioritization and different selection probabilities.

In search of the wisdom web

Web intelligence offers a new direction for scientific research and development, pushing technology toward manipulating the meaning of data and creating a distributed intelligence that can actually get things done. WI explores the fundamental and practical impact that artificial intelligence and advanced information technology will have on the next generation of Web-empowered systems, services, and environments.The Web significantly affects both academic research and everyday life, revolutionizing how we gather, store, process, present, share, and use information. It offers opportunities and challenges in many areas, including business, commerce, finance, and research and development.The next-generation Web will go beyond improved information search and knowledge queries and will help people achieve better ways of living, working, playing, and learning. To fulfill its potential, the intelligent Webs design and development must incorporate knowledge from existing disciplines, such as artificial intelligence and information technology, in a totally new domain.

Adaptive image segmentation with distributed behavior-based agents

Presents an autonomous agent-based image segmentation approach. In this approach, a digital image is viewed as a two-dimensional cellular environment which the agents inhabit and attempt to label homogeneous segments. In so doing, the agents rely on some reactive behaviors such as breeding and diffusion. The agents that are successful in finding the pixels of a specific homogeneous segment will breed offspring agents inside their neighboring regions. Hence, the offspring agents will become likely to find more homogeneous-segment pixels. In the mean time, the unsuccessful agents will be inactivated, without further search in the environment.

An evolutionary autonomous agents approach to image feature extraction

This paper presents a new approach to image feature extraction which utilizes evolutionary autonomous agents. Image features are often mathematically defined in terms of the gray-level intensity at image pixels. The optimality of image feature extraction is to find all the feature pixels from the image. In the proposed approach, the autonomous agents, being distributed computational entities, operate directly in the 2-D lattice of a digital image and exhibit a number of reactive behaviors. To effectively locate the feature pixels, individual agents sense the local stimuli from their image environment by means of evaluating the gray-level intensity of locally connected pixels, and accordingly activate their behaviors. The behavioral repository of the agents consists of: 1) feature-marking at local pixels and self-reproduction of offspring agents in the neighboring regions if the local stimuli are found to satisfy feature conditions, 2) diffusion to adjacent image regions if the feature conditions are not held, or 3) death if the agents exceed their life span. As part of the behavior evolution, the directions in which the agents self-reproduce and/or diffuse are inherited from the directions of their selected high-fitness parents. Here the fitness of a parent agent is defined according to the steps that the agent takes to locate an image feature pixel.

An adaptive user interface based on personalized learning

This adaptive user interface provides individualized, just-in-time assistance to users by recording user interface events and frequencies, organizing them into episodes, and automatically deriving patterns. It also builds, maintains, and makes suggestions based on user profiles.

Off-line recognition of Chinese handwriting by multifeature and multilevel classification

In this paper, an off-line recognition system based on multifeature and multilevel classification is presented for handwritten Chinese characters. Ten classes of multifeatures, such as peripheral shape features, stroke density features, and stroke direction features, are used in this system. The multilevel classification scheme consists of a group classifier and a five-level character classifier, where two new technologies, overlap clustering and Gaussian distribution selector are developed. Experiments have been conducted to recognize 5,401 daily-used Chinese characters. The recognition rate is about 90 percent for a unique candidate, and 98 percent for multichoice with 10 candidates.

Brain Informatics

Brain informatics (BI) is an emerging interdisciplinary and multidisciplinary research field that focuses on studying the mechanisms underlying the human information processing system. BI investigates the essential functions of the brain, ranging from perception to thinking, and encompassing such areas as multiperception, attention, emotion, memory, language, computation, heuristic search, reasoning, planning, decision making, problem solving, learning, discovery, and creativity. This special issue presents some of the best works being developed worldwide that deal with the new challenges of BI from an intelligent systems perspective.

Intelligent Technologies for Information Analysis

1) The Alchemy of Intelligent IT (iIT) (Ning Zhong, Jiming Liu) Part I Emerging Data Mining Technology ======================================= 2) Grid-Based Data Mining and Knowledge Discovery (Mario Cannataro, Antonio Congiusta, Carlo Mastroianni, Andrea Pugliese, Domenico Talia, Paolo Trunfio) 3) The MiningMart Approach to Knowledge Discovery in Databases (Katharina Morik, Martin Scholz) 4) Ensemble Methods and Rule Generation (Yongdai Kim, Jinseog Kim, Jongwoo Jeon) 5) Evaluation Scheme for Exception Rule/Group Discovery (Einoshin Suzuki) 6) Data Mining for Direct Marketing (Ning Zhong, Yiyu Yao, Chunnian Liu, Chuangxin Ou, Jiajin Huang) Part II Data Mining for Web Intelligence ========================================= 7) Mining for Information Discovery on the Web (Hwanjo Yu, An Hai Doan, Jiawei Han) 8) Mining Web Logs for Actionable Knowledge (Qiang Yang, Charles X. Ling, Jianfeng Gao) 9) Discovery of Web Robot Sessions Based on Their Navigational Patterns (Pang-Ning Tan, Vipin Kumar) 10) Web Ontology Learning and Engineering (Roberto Navigli, Paola Velardi, Michele Missikoff) 11) Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis (Richard Cole, Peter Eklund, Florence Amardeilh) 12) Graph Discovery and Visualization from Textual Data (Vincent Dubois, Mohamed Quafafou) Part III Emerging Agent Technology =================================== 13) Agent Networks: Topological and Clustering Characterization (Xiaolong Jin, Jiming Liu) 14) Finding the Best Agents for Cooperation (Francesco Buccafurri, Domenico Rosaci, Giuseppe L.M. Sarne, Luigi Palopoli) 15) Constructing Hybrid Intelligent Systems for Data Mining from Agent Perspectives (Zili Zhang, Zhengqi Zhang) 16) Making Agents Acceptable to People (Jeffrey M. Bradshaw, Patrick Beautement, Maggie R. Breedy, Larry Bunch, Sergey V. Drakunov, Paul J. Feltovich, Robert R. Hoffman, Renia Jeffers, Matthew Johnson, Shriniwas Kulkarnt, James Lott, Anil K. Raj, Niranjan Suri, Andrzej Uszok) Part IV Emerging Soft Computing Technology =========================================== 17) Constraint-Based Neural Network Learning for Time Series Predictions (Benjamin W. Wah, Minglun Qian) 18) Approximate Reasoning in Distributed Environments (Andrzej Skowron) 19) Soft Computing Pattern Recognition, Data Mining, and Web Intelligence (Sankar K. Pal, Sushmita Mitra, Pabitra Mitra) 20) Dominance-Based Rough Set Approach to Knowledge Discovery (I) (Salvatore Greco, Benedetto Matarazzo, Roman Slowinski) 21) Dominance-Based Rough Set Approach to Knowledge Discovery (II) (Salvatore Greco, Benedetto Matarazzo, Roman Slowinski) Part V Statistical Learning Theory =================================== 22) Mining Dependence Structures (I) -- A General Statistical Learning Perspective -- (Lei Xu) 23) Mining Dependence Strucutres (II) -- An Independence Analysis Perspective -- (Lei Xu)