19118237
A machine learning approach for the curation of biomedical literature: KDD Cup 2002 (task 1)
In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naïve Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources.
2847866
A Machine Learning Approach for the Curation of Biomedical Literature
In the field of the biomedical sciences there exists a vast repository of information located within large quantities of research papers. Very often, researchers need to spend considerable amounts of time reading through entire papers before being able to determine whether or not they should be curated (archived). In this paper, we present an automated text classification system for the classification of biomedical papers. This classification is based on whether there is experimental evidence for the expression of molecular gene products for specified genes within a given paper. The system performs pre-processing and data cleaning, followed by feature extraction from the raw text. It subsequently classifies the paper using the extracted features with a Naive Bayes Classifier. Our approach has made it possible to classify (and curate) biomedical papers automatically, thus potentially saving considerable time and resources. The system proved to be highly accurate, and won honourable mention in the KDD Cup 2002 task 1.
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.
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.
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.
2536263
Multi-scale Voxel-Based Morphometry Via Weighted Spherical Harmonic Representation
Although the voxel-based morphometry (VBM) has been widely used in quantifying the amount of gray matter of the human brain, the optimal amount of registration that should be used in VBM has not been addressed. In this paper, we present a novel multi-scale VBM using the weighted spherical harmonic (SPHARM) representation to address the issue. The weighted-SPHARM provides the explicit smooth functional representation of a true unknown cortical boundary. Based on this new representation, the gray matter tissue density is constructed using the Euclidean distance map from a voxel to the estimated smooth cortical boundary. The methodology is applied in localizing abnormal cortical regions in a group of autistic subjects.
17699029
Spherical parameterization for 3D surface analysis in volumetric images
We propose a new spherical parameterization algorithm for general genus-zero surfaces that aims to control the area and length distortions at the same time. Prior work either considers only the length distortion or applies only to volumetric surfaces. Our algorithm starts from an initial parameterization, performs local and global smoothing steps alternately to improve the mapping quality, and converges to the solution. The local method minimizes the area distortion at a local submesh by solving a linear system as well as controls the length distortion. The global method attempts to reduce the area distortion by equalizing its distribution over the whole sphere. We perform experimental studies showing the effectiveness of the technique. Our new algorithm has many applications in medical image analysis, computer vision, graphics and multimedia databases.
23994030
Fast Association Discovery in Derivative Transaction Collections
Abstract. Association discovery from a transaction collection is an important data-mining task. We study a new problem in this area whose solution can provide users with valuable association rules in some relevant collections: association discovery in derivative transaction collections. In this problem, we are given association rules in two transaction collections D1 and D2, and aim to find new association rules in derivative transaction collections D1∖D2, D1∩D2, D2∖D1 and D1∪D2. Direct application of existing algorithms can solve this problem, but in an expensive way. We propose an efficient solution through making full use of already discovered information, taking advantage of the relationships existing among relevant collections, and avoiding unnecessary but expensive support-counting operations by scanning databases. Experiments on well-known synthetic data show that our solution consistently outperforms the naive solution by factors from 2 to 3 in most cases. We also propose an efficient parallelization of our approach, as parallel algorithms are often interesting and necessary in the area of data mining.
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.
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.