Accurate ensemble pruning with PL-bagging

Ensemble pruning deals with the selection of base learners prior to combination in order to improve prediction accuracy and efficiency. In the ensemble literature, it has been pointed out that in order for an ensemble classifier to achieve higher prediction accuracy, it is critical for the ensemble classifier to consist of accurate classifiers which at the same time diverse as much as possible. In this paper, a novel ensemble pruning method, called PL-bagging, is proposed. In order to attain the balance between diversity and accuracy of base learners, PL-bagging employs positive Lasso to assign weights to base learners in the combination step. Simulation studies and theoretical investigation showed that PL-bagging filters out redundant base learners while it assigns higher weights to more accurate base learners. Such improved weighting scheme of PL-bagging further results in higher classification accuracy and the improvement becomes even more significant as the ensemble size increases. The performance of PL-bagging was compared with state-of-the-art ensemble pruning methods for aggregation of bootstrapped base learners using 22 real and 4 synthetic datasets. The results indicate that PL-bagging significantly outperforms state-of-the-art ensemble pruning methods such as Boosting-based pruning and Trimmed bagging.     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

低渗透率地层渗透率的确定方法

结合大庆油田外围低渗透率地层的地质特点,根椐渗流理论和岩心分析资料,分析了地层渗透率与孔隙度、组成地层的各种矿物含量以及泥质含量之间的关系,建立了不同矿物组合地层的渗透率计算方程。用本方法计算的和岩心分析的渗透率进行了对比,二者之间具有较好的一致性。     

Numerical simulation of typical contact situations of brake friction materials

In the paper, a model typical for contact situations of automotive brakes is established based on the method of movable cellular automata. The processes taking place at local contacts in an automotive brake system are analysed. Based on microscopic and micro-analytical observations, the following contact situations were simulated: (i) a couple of ferritic steel against pearlitic steel, both covered by an oxide layer mixed with graphite nanoparticles and (ii) the same situation but without oxide layers. The results of calculated mean coefficients of friction of the oxide-on-oxide contact correspond well to expected values for a real braking system, whereas steel-on-steel contact are twice as high. This allows one to make some conclusions; for example, oxide formation will take place more quickly than friction layer elimination, and finally this is responsible for the stabilisation of the coefficient of friction.     

Adaptive user modelling in intelligent telephone and email assistants

With the burgeoning complexity and capabilities of modern information appliances and services, user modelling is becoming an increasingly important research area. Simple user profiles already personalise many software products and consumer goods such as digital TV recorders and mobile phones. A user model should be easy to initialise, and it must adapt in the light of interaction with the user. In many cases, a large amount of training data is needed to generate a user model, and adaptation is equivalent to retraining the system. This paper briefly outlines the user modelling problem and work done at BTexact on an intelligent personal assistant (IPA) which incorporates a user profile. We go on to describe FILUM, a more flexible method of user modelling, and show its application to the telephone assistant and email assistant components of the IPA, with tests to illustrate its usefulness.     

Preparation of CeO2-ZrO2 mixed oxide with high surface area and high thermal stability

Ceria-zirconia solid particles have been recognized as a key material of the automotive exhaust catalysts since they can release and uptake oxygen owing to the rapid reversible oxidation states of cerium between Ce³⁺ and Ce⁴⁺. Several methods have recently been described to prepare the CeO₂-ZrO₂ solid particles used in the catalysts. In this paper, a new coprecipitation method is used to prepare the CeO₂-ZrO₂ solid particles. The Ce-Zr alcogel is dried and calcined in flowing N₂ not in flowing air under atmospheric pressure. The results show that the ceria-zirconia sample calcined at 650 °C has high surface area over 90 m²g⁻¹, which drops to 40 m²g⁻¹ following treatment at 900°C.     

Lattice structure on some fuzzy algebraic systems

In this paper, we study the lattice structure of some fuzzy algebraic systems such as (G-)fuzzy groups, some fuzzy ordered algebras and fuzzy hyperstructures. We prove that under suitable conditions, these structures form a distributive or modular lattice. This research partially is supported by the “ Fuzzy Systems and its Applications Center of Excelence, Shahid Bahonar University of Kerman, Iran”.