Comments on the benchmarks in "A proposal for improving the accuracy of Linguistic Modeling" and related articles

In the above paper by Cordon and Herrara (IEEE Trans. Fuzzy Syst., vol. 8, p. 335-44, 2000), the so-called accurate linguistic modeling (ALM) method was proposed to improve the accuracy of linguistic fuzzy models. A number of examples are given to demonstrate the benefits of the approach. We show that: 1) these examples are not suitable as benchmarks or demonstrators of nonlinear modeling techniques and 2) better results can be obtained by using both standard regression tools as well as other fuzzy modeling techniques. We argue that benchmark examples that are used in articles to demonstrate the effectiveness of fuzzy modeling techniques should be selected with great care. Critical analysis of the results should be made and linear models should be regarded as a lower bound on the acceptable performance.     

Author's reply [to comments on 'A proposal to improve the accuracy of linguistic modelling']

In our opinion, there are two main concerns in Roubos and Babugka's note, that are summarized as follows. 1) The kinds of problems used in our paper to test the algorithm proposed in "A proposal to improve the accuracy of linguistic modeling" and other studies. The authors claim that they are very simple to be considered as benchmarks for nonlinear modeling techniques. 2) The interpretability of the different kinds of models considered. Roubos and Babuska think that there is no difference between the interpretability of fuzzy linguistic models, Takagi-Sugeno-Kang (TSK) fuzzy models, and mathematical formulations (linear models, in this case). We agree with some of the opinions of the authors of the note but not with some others.     

A method for improving the accuracy of data mining classification algorithms

In this paper we introduce a method called CL.E.D.M. (CLassification through ELECTRE and Data Mining), that employs aspects of the methodological framework of the ELECTRE I outranking method, and aims at increasing the accuracy of existing data mining classification algorithms. In particular, the method chooses the best decision rules extracted from the training process of the data mining classification algorithms, and then it assigns the classes that correspond to these rules, to the objects that must be classified. Three well known data mining classification algorithms are tested in five different widely used databases to verify the robustness of the proposed method.     

A meta-heuristic approach for improving the accuracy in some classification algorithms

Current classification algorithms usually do not try to achieve a balance between fitting and generalization when they infer models from training data. Furthermore, current algorithms ignore the fact that there may be different penalty costs for the false-positive, false-negative, and unclassifiable types. Thus, their performance may not be optimal or may even be coincidental. This paper proposes a meta-heuristic approach, called the Convexity Based Algorithm (CBA), to address these issues. The new approach aims at optimally balancing the data fitting and generalization behaviors of models when some traditional classification approaches are used. The CBA first defines the total misclassification cost (TC) as a weighted function of the three penalty costs and the corresponding error rates as mentioned above. Next it partitions the training data into regions. This is done according to some convexity properties derivable from the training data and the traditional classification method to be used in conjunction with the CBA. Next the CBA uses a genetic approach to determine the optimal levels of fitting and generalization. The TC is used as the fitness function in this genetic approach. Twelve real-life datasets from a wide spectrum of domains were used to better understand the effectiveness of the proposed approach. The computational results indicate that the CBA may potentially fill in a critical gap in the use of current or future classification algorithms.     

A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

Recommender systems represent a class of personalized systems that aim at predicting a user’s interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.     

Linguistic models and linguistic modeling

The study is concerned with a linguistic approach to the design of a new category of fuzzy (granular) models. In contrast to numerically driven identification techniques, we concentrate on budding meaningful linguistic labels (granules) in the space of experimental data and forming the ensuing model as a web of associations between such granules. As such models are designed at the level of information granules and generate results in the same granular rather than pure numeric format, we refer to them as linguistic models. Furthermore, as there are no detailed numeric estimation procedures involved in the construction of the linguistic models carried out in this way, their design mode can be viewed as that of a rapid prototyping. The underlying algorithm used in the development of the models utilizes an augmented version of the clustering technique (context-based clustering) that is centered around a notion of linguistic contexts-a collection of fuzzy sets or fuzzy relations defined in the data space (more precisely a space of input variables). The detailed design algorithm is provided and contrasted with the standard modeling approaches commonly encountered in the literature. The usefulness of the linguistic mode of system modeling is discussed and illustrated with the aid of numeric studies including both synthetic data as well as some time series dealing with modeling traffic intensity over a broadband telecommunication network.     

A linear hybrid methodology for improving accuracy of time series forecasting

Modeling and forecasting of time series data are integral parts of many scientific and engineering applications. Increasing precision of the performed forecasts is highly desirable but a difficult task, facing a number of mathematical as well as decision-making challenges. This paper presents a novel approach for linearly combining multiple models in order to improve time series forecasting accuracy. Our approach is based on the assumption that each future observation of a time series is a linear combination of the arithmetic mean and median of the forecasts from all participated models together with a random noise. The proposed ensemble is constructed with five different forecasting models and is tested on six real-world time series. Obtained results demonstrate that the forecasting accuracies are significantly improved through our combination mechanism. A nonparametric statistical analysis is also carried out to show the superior forecasting performances of the proposed ensemble scheme over the individual models as well as a number of other forecast combination techniques.     

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.     

A unified framework for improving the accuracy of all holistic face identification algorithms

Reconstructing the challenging human face identification process as a stability problem, we show that Electoral College can be used as a framework that provides a significantly enhanced face identification process by improving the accuracy of all holistic algorithms. The results are demonstrated by extensive experiments on benchmark face databases applying the Electoral College framework embedded with standard baseline and newly developed face identification algorithms.     

一种提高室内定位精度的算法

室内定位技术对于室内物品的实时监管具有重要的实际应用价值。为了进行室内物品的精确定位,文中建立了一种基于距离的最优估计定位模型,并引入变尺度(DFP)算法对模型进行求解,从而达到提高坐标精度的目的。本设计首先对原始距离信息利用最小二乘估计、三次样条插值法来提高测距精度;其次通过三圆公共弦交点法确定出初始坐标值;然后把空间几何关系转换为无约束极小值问题,再采用DFP法对初始坐标进一步精化,以得出更为精确的坐标值。最后通过两种模拟实验对算法进行验证,结果表明:引入最优估计理论的室内定位算法,具有更高的定位精度。     

一种改善直方图均衡化精度的方法

本文对直方图规定化的组映射规则(groupmappinglawGML)作了修改,并用于直方图均衡化,提出了较详细的算法,结果证明该算法可改善直方图均衡化的精度。     

A method for improving measurement accuracy of cylinders in dimensional CT metrology

Measurement quality of industrial cone beam X-ray computed tomography (CT) is influenced by many types of artefacts, therefore, industrial-level accuracy is difficult to attain. In order to avoid the influences of these artefacts, this paper proposes a new method for measuring cylindrical surface that is a common geometric structure in mechanical parts. The proposed method fits cylindrical surface from a sinogram image directly. Compared with a standard way for dimensional CT metrology, higher measurement accuracy and less measurement time can be achieved. Mainly, the method consists of edge points detection of a sinogram image and cylindrical surface fitting. The performance of the method is evaluated by use of both simulation data and actual data.     

Semantic spaces for improving language modeling

Language models are crucial for many tasks in NLP (Natural Language Processing) and n-grams are the best way to build them. Huge effort is being invested in improving n-gram language models. By introducing external information (morphology, syntax, partitioning into documents, etc.) into the models a significant improvement can be achieved. The models can however be improved with no external information and smoothing is an excellent example of such an improvement.In this article we show another way of improving the models that also requires no external information. We examine patterns that can be found in large corpora by building semantic spaces (HAL, COALS, BEAGLE and others described in this article). These semantic spaces have never been tested in language modeling before. Our method uses semantic spaces and clustering to build classes for a class-based language model. The class-based model is then coupled with a standard n-gram model to create a very effective language model.Our experiments show that our models reduce the perplexity and improve the accuracy of n-gram language models with no external information added. Training of our models is fully unsupervised. Our models are very effective for inflectional languages, which are particularly hard to model. We show results for five different semantic spaces with different settings and different number of classes. The perplexity tests are accompanied with machine translation tests that prove the ability of proposed models to improve performance of a real-world application.     

A technique for evaluating and improving the semantic transparency of modeling language notations

Software and Systems Modeling - The notation of a modeling language is of paramount importance for its efficient use and the correct comprehension of created models. A graphical notation,...     

On improving the accuracy of the Hough transform

The subject of this paper is very high precision parameter estimation using the Hough transform. We identify various problems that adversely affect the accuracy of the Hough transform and propose a new, high accuracy method that consists of smoothing the Hough arrayH(, ) prior to finding its peak location and interpolating about this peak to find a final sub-bucket peak. We also investigate the effect of the quantizations and ofH(, ) on the final accuracy. We consider in detail the case of finding the parameters of a straight line. Using extensive simulation and a number of experiments on calibrated targets, we compare the accuracy of the method with results from the standard Hough transform method of taking the quantized peak coordinates, with results from taking the centroid about the peak, and with results from least squares fitting. The largest set of simulations cover a range of line lengths and Gaussian zero-mean noise distributions. This noise model is ideally suited to the least squares method, and yet the results from the method compare favorably. Compared to the centroid or to standard Hough estimates, the results are significantly better—for the standard Hough estimates by a factor of 3 to 10. In addition, the simulations show that as and are increased (i.e., made coarser), the sub-bucket interpolation maintains a high level of accuracy. Experiments using real images are also described, and in these the new method has errors smaller by a factor of 3 or more compared to the standard Hough estimates.     

一种提高网络寿命与节点定位性能的WSNs分群算法

针对节点随机布设的大规模无线传感器网络,为延长网络的寿命并提高对辐射源的定位精度,提出了一种新的分群算法。该算法综合考虑网络能耗、节点的能耗均衡、辐射源的定位精度等因素,利用改进的离散粒子群算法优化选取出最优节点集并组成相应的群参与最终的定位。以RSSI（Received Signal Strength Indication）/TDOA（Time Difference of Arrive）两轮定位算法为例,对该分群算法进行了仿真分析,结果表明该算法在保证群内节点多跳连通的情况下,减少了网络能耗,同时提高了对辐射源的定位精度。     

An aftertreatment technique for improving the accuracy of Adomian's decomposition method

Adomian's decomposition method (ADM) is a nonnumerical method which can be adapted for solving nonlinear ordinary differential equations. In this paper, the principle of the decomposition method is described, and its advantages as well as drawbacks are discussed. Then an aftertreatment technique (AT) is proposed, which yields the analytic approximate solution with fast convergence rate and high accuracy through the application of Padé approximation to the series solution derived from ADM. Some concrete examples are also studied to show with numerical results how the AT works efficiently.