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A novel feature selection approach is proposed for data space defined over continuous features, which obtains a subset of features,such that it can discriminate class labels of objects and its discriminant ability is not inferior to that of the original features,so to effectively improve the learning performance and intelligibility of the classification model. According to the spatial distribution...
The paper tried to build the classification model of cashew nuts and had analyzed the shape parameters of cashew nuts, and the paper concluded that there was a clear linear relationship between the length and height of cashew nuts and there was not a clear linear relationship between the length and thickness of cashew nuts or between the height and thickness of cashew nuts. Because of the restrictions...
Since the blog service brings a wealth of information resources, blog search and classification are showing their great research value. This paper focuses on the blog classification on the personal vs. official facet. Our system adopts a two-stage strategy; in training model, lexicons are built automatically; in classification model, scoring and ranking are carried out orderly. Our experimental results...
A fundamental problem in machine learning is to discriminate a representative set of features on which to construct a classification model for a particular task. This paper presents a feature selection algorithm RF-MI for multiple classes based on ReliefF algorithm and Mutual Information (MI) measure. RF-MI algorithm gets a feature subset by excluding irrelevant and redundant features from original...
Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for...
Restricted Bayesian network is an efficient classification model. However, so far some researchers still attempt to improve the performance by considering directions of edges, because traditional learning method merely takes into account log likelihood, which is not suitable for learning classifiers, when learning a tree topological structure. In this paper, we analyze the search spaces and the equivalent...
Aiming at the shortages of the existing data-mining model for forecasting the industry security, a classification model based on rough sets and BP neural network (BPNN) is put forward in this paper. First, the theory of rough set is applied to pick up and reduce the index attributes. Then, the training samples are sent to the BPNN to train and learn. After that, the sorts of the coal industry security...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
This paper addresses an issue of incorporating topic knowledge to improve Chinese word sense disambiguation. The key is how to learn topic knowledge as features in the design of classifiers for disambiguating word senses. This paper presents two solutions to learn topic knowledge. In the first solution, a Chinese domain knowledge dictionary named NEUKD is used to generate domain feature set. However,...
Typically, two aspects are used to evaluate the quality of a classification model, i.e., the classifying accuracy and the interpretability. The recently developed sparse representation-based face recognition techniques, though achieving high accuracies, rarely concern the interpretability of the classification model. In particular, the obtained sparseness, in terms of the sparse representative coefficient...
The decision tree algorithm, an analytical and predictive model to describe datum under the influence of different variables, has been successfully put to use in diagnosing and treating certain diseases. To meet the requirements and characteristics of the clinical classification for the hand venule of baby pneumonia, this paper proposes a new classification model for the hand venule of baby pneumonia...
At present, detecting customs declaration frauds with limited examination of imported goods by available scarce resources is posing considerable challenge to the customs authorities world over. Data mining techniques could be utilized to sift through the past data and develop predictive model for examination of limited goods with higher probability of fraud. This paper puts forward a classification...
In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy...
The performance of a classification model depends not only on the algorithm by which the model is learned, but also on the training set. Manual annotation of the training data is a tedious and time consuming job. In order to overcome the problem of laborious hand-labeling of a large training set, a set of techniques called semi-supervised learning was designed. Co-training is one of the major semi-supervised...
A novel classification method based on relevance vector machine with genetic algorithm is presented in the paper. In the model, genetic algorithm is applied to gain the suitable training parameters of relevance vector machine. State classification of roll bearing is applied to testify the classification ability of the proposed method, and state classification data of roll bearing are given. The experimental...
the research for traditional Chinese medicine's warm and cold natures classification is a significative thing for clinical. The paper put forward a model to classify Chinese herbs' warm and cold natures. The first, data preprocessing; The second, extract the typical M/Z(mass-to-charge ratio) character values from these mice of the train sample have been given the warm or cold natures which have been...
A growing number of organizations and individuals improve efficiency by utilizing geographical data. When the geographical data are updated, the changed information must be transferred to the end-users to keep their client-databases current. Distinguishing and identifying the change types of the geographical objects is the premise of transferring the change information. But now the methods for classifying...
Assessing scientific inquiry skills in INQPRO, a scientific inquiry learning environment developed in this research work, presents two major challenges: (i) identifying a set of important features from a series of student interactions for assessment of scientific inquiry skills is difficult. Such difficulty stemmed not only because there exists ways a student interacts with the scientific inquiry...
This paper proposes a new interpretable neuro-fuzzy classification mechanism. The proposed neuro-fuzzy structure is different from other data analysis mechanisms previously invented in pattern recognition. General mechanisms focus mainly on creating predictive data models whereas some useful information inside the process may be ignored. The proposed mechanism is designed based on the consideration...
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
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