42142199
A discretization method for rough sets theory
The Rough Sets Theory, as a powerful knowledge-mining tool, has been widely applied to acquire knowledge in the medical, engineering and financial domains. However, this powerful tool cannot be applied to real-world classification tasks involving continuous features. This requires the utilization of discretization methods. ChiMerge, since it was first proposed in 1992, has become a widely used discretization method. The Chi2 algorithm is one modification to the ChiMerge algorithm. It automates the discretization process by introducing an inconsistency rate as the stopping criterion and it automatically selects the significance level. In addition, it incorporates a finer phase aimed at feature selection to broaden the applications of the ChiMerge algorithm. However, both the ChiMerge and the Chi2 algorithms do not consider the inaccuracy inherent in the merging criterion. In addition, the user-defined inconsistency rate of the Chi2 algorithm also brings about inaccuracy to the discretization process which leads to over-merging. To overcome these two drawbacks, a new discretization method, termed as the modified Chi2 algorithm, is proposed. Comparison studies carried out on the predictive accuracy shows that this modified Chi2 algorithm outperforms the original Chi2 algorithm. Thus, a completely automatic discretization method for Rough Sets Theory has been realized.
9193368
Economic and financial prediction using rough sets model
Abstract A state-of-the-art review of the literature related to economic and financial prediction using rough sets model is presented, with special emphasis on the business failure prediction, database marketing and financial investment. These three applications require the accurate prediction of the future states based on the identification of patterns in the historical data. In addition, the historical data are in the format of a multi-attribute information table. All these conditions suit the rough sets model, an effective tool for multi-attribute classification problems. The different rough sets models and issues concerning the implementation of rough sets model – indicator selection, discretization and validation test, are also discussed in this paper. This paper will demonstrate that rough sets model is applicable to a wide range of practical problems pertaining to economic and financial prediction. In addition, the results show that the rough sets model is a promising alternative to the conventional methods for economic and financial prediction.
6411349
A Modified Chi2 Algorithm for Discretization
Since the ChiMerge algorithm was first proposed by Kerber (1992), it has become a widely used and discussed discretization method. The Chi2 algorithm is a modification to the ChiMerge method. It automates the discretization process by introducing an inconsistency rate as the stopping criterion and it automatically selects the significance value. In addition, it adds a finer phase aimed at feature selection to broaden the applications of the ChiMerge algorithm. However, the Chi2 algorithm does not consider the inaccuracy inherent in ChiMerge's merging criterion. The user-defined inconsistency rate also brings about inaccuracy to the discretization process. These two drawbacks are first discussed in this paper and modifications to overcome them are then proposed. By comparison, results with the original Chi2 algorithm using C4.5, the modified Chi2 algorithm, performs better than the original Chi2 algorithm. It becomes a completely automatic discretization method.
6400431
Applying rough sets to market timing decisions
A lot of research has been done to predict economic development. The problem studied here is about the stock prediction for use of investors. More specifically, the stock market's movements will be analyzed and predicted. We wish to retrieve knowledge that could guide investors on when to buy and sell. 
 
Through a detailed case study on trading S&P 500 index, rough sets is shown to be an applicable and effective tool to achieve this goal. Some problems concerning time series transformation, indicator selection, trading system building in real implementation are also discussed.
811735
Classifying Market States with WARS
In this paper, a new indicator - WARS (Weighted Accumulated Reconstruction Series) at classifying the state of financial market, either trending state or mean-reverting state, was presented. Originated from the computation of Entropy, this new indicator was found to be able to reflect the market behavior accurately and easily. The algorithm of generating WARS and its meaning related to Entropy were introduced and some comparison results between WARS and the Daily Profit Curve were listed. As a new indicator, WARS also can be used to build a trading system - to provide buy, sell and hold signals. Through the application on S&P 500 index, it was verified to be effective and was a promising indicator.
635773
Surface Alignment of 3D Spherical Harmonic Models: Application to Cardiac MRI Analysis
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected 3D objects and has been used in many applications in medical imaging. Previous SPHARM techniques use the first order ellipsoid for establishing surface correspondence and aligning objects. However, this first order information may not be sufficient in many cases; a more general method for establishing surface correspondence would be to minimize the mean squared distance between two corresponding surfaces. In this paper, a new surface matching algorithm is proposed for 3D SPHARM models to achieve this goal. This algorithm employs a useful rotational property of spherical harmonic basis functions for a fast implementation. Applications of medical image analysis (e.g., spatio-temporal modeling of heart shape changes) are used to demonstrate this approach. Theoretical proofs and experimental results show that our approach is an accurate and flexible surface correspondence alignment method.
15158980
Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics
We present an approach for large-scale modeling of parametric surfaces using spherical harmonics (SHs). A standard least square fitting (LSF) method for SH expansion is not scalable and cannot accurately model large 3D surfaces. We propose an iterative residual fitting (IRF) algorithm, and demonstrate its effectiveness and scalability in creating accurate SH models for large 3D surfaces. These large-scale and accurate parametric models can be used in many applications in computer vision, graphics, and biomedical imaging. As a simple extension of LSF, IRF is very easy to implement and requires few machine resources.
15818599
Efficient Registration of 3D SPHARM Surfaces
We present SHREC, an efficient algorithm for registration of 3D SPHARM (spherical harmonic) surfaces. SHREC follows the iterative closest point (ICP) registration strategy, and alternately improves the surface correspondence and adjusts the object pose. It establishes the surface correspondence by aligning the underlying SPHARM parameterization. It employs a rotational property of the harmonic expansion to accelerate its step for parameterization rotation. It uses a hierarchical icosahedron approach to sample the rotation space and searches for the best parameterization that matches the template. Our experimental results show that SHREC can not only create more accurate registration than previous methods but also do it efficiently. SHREC is a simple, efficient and general registration method, and has a great potential to be used in many shape modeling and analysis applications.
13985475
A clustering-based approach for prediction of cardiac resynchronization therapy
This paper presents a method for predicting pacing sites in the left ventricle of a heart and its result can be used to assist device programming in cardiac resynchronization therapy (CRT), which is a widely adopted therapy for heart failure patients. In a traditional CRT device deployment, pacing sites are selected without quantitative prediction. That runs the risk of suboptimal benefits. In this work, a surface tracking method is proposed to describe the ventricular wall motion and a hierarchical agglomerative clustering technique is applied to radial motion series to find candidate pacing sites. Using clinical MRI data in our experiments, we show that the proposed method performs as well as we expect. Our approach can not only effectively identify suitable pacing sites, but also distinguish patients from normals perfectly to help medical diagnosis.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.