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In BioWorld, a medical intelligent tutoring system, novice physicians are tasked with solving virtual patient cases. Whilst the importance of modeling and predicting clinical reasoning is recognized, an important aspect of the learner contribution remains unexplored — the written case summary prepared by the learner. The premise of investigating the case summaries is that it captures the thought and...
Many advancement is made in recent days and number of techniques are proposed by different researchers for processing and extracting knowledge from big data. But to evaluate the consistency in extracted model is always questionable. In this paper we are presenting two techniques for measuring the consistency between extracted model and predicting their applicability. In this paper, Meta learning based...
Land cover classification accuracy assessments are frequently limited to an error matrix, which derived from location-independent measures and consequently doesn't provide any information about the spatial distribution of the error. The objective of this work is to present a methodology for mapping the spatial distribution of classification errors based on stochastic simulation and that takes into...
The importance to financial institutions of accurately evaluating the credit risk posed by their loan granting decisions cannot be underestimated; it is underscored by recent credit assessment failures that contributed greatly to the so-called "great recession" of the late 2000s. The paper compares the classification accuracy rates of several traditional and computational intelligence methods...
Minimum redundancy maximum relevancy (mRMR) is one of the successful criteria used by many feature selection techniques to evaluate the discriminating abilities of the features. We combined dynamic sample space with mRMR and proposed a new feature selection method. In each iteration, the weighted mRMR values are calculated on dynamic sample space consisting of the current unlabelled samples. The feature...
From a new view of financial distress concept drift, this paper attempts to put forward a new method for dynamic financial distress prediction modeling based on slip time window and multiple support vector machines (SVMs). A new algorithm is designed to dynamically select the proper time window to handle concept drift, and then a dynamic classifier selection method is used to build a combined model...
The traditional TF-IDF algorithm is a common method that is used to measure feature weight in text categorization. However, the algorithm doesn't take the distribution of feature terms in inter-class and intra-class into consideration. Consequently, the algorithm can't effectively weigh the distribution proportion of feature items. In order to solve this problem, information entropy in inter-class...
This paper builds a forecasting model of service marketing which was based on cluster analysis combining with decision tree, it depicts K-means algorithm, C5.0 algorithm of decision tree and design of building model, and applies the model on predicting whether a region users will accept the interactive service of cable television, by this way it finds the users group which shows the highest response...
Grids systems are enormous environments that allow to users to share their resource and collaborate for executing of consumer's job. Recently, the need for interoperability among different grid systems and using online updated grid resource information centers for both market-base grids and non-economic grids has become increased. In this paper we specifically focus on online updating resource information...
To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate...
This paper studies the nonlinear relation between the balance of trade and the affection factors. The nonlinear regression model for the balance of trade is constructed by random forest (RF) method. Moreover, the ranking of importance for affection factors is given out. The empirical results in this paper reveal the balance of trade in China is affected mainly by M2, GDP, CPI, Exchange reserves, M1...
Exploring micro RNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method for identifying functional miRNA-mRNA modules from heterogeneous expression...
Making use full of multi-source and multi-temporal information to extract richer and interesting information is a tendency in analysis of remote sensing images. In this paper, spatial and temporal contextual classification based on Markov Random Field (MRF) is used to classify ecological function vegetation in Poyang Lake. The results show that spatial and temporal neighborhood complementary information...
Aim at the low accuracy of intrusion detection system, to analysis the Bayesian classification algorithm and give some improvements, with the experimental data of kddcup99, in order to find a reasonable data pre-processing methods and more effective classification algorithm to improve the accuracy of intrusion detection system.
Naïve Bayesian classifier is a simple classification method based on Bayes statistics, which is one of the most popular classifiers and has been successfully applied to many fields. To improve the generalization ability of the naïve Bayesian classifier, discriminative learning of the naïve Bayesian classifier is researched. In this paper, a parameter learning algorithm AENB of the naïve Bayesian...
Diversity among base classifiers is known to be a necessary condition for improving ensemble learning performance. In this paper, methods of selective ensemble learning including hill-climbing selection, ensemble forward sequential selection, ensemble backward sequential selection and clustering selection are studied. To measure the diversity among base classifiers in ensemble learning, the entropy...
The paper compares the classification performance rate of eight models: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), case-base reasoning (CBR), and three decision trees (DTs). We build models and test their classification accuracy rates on a historical data set provided by a German financial institution. The data set contains...
This paper presents a neural network classifier based on fuzzy ARTMAP with conflict-resolving strategy. The proposed model explicitly resolves overlaps among prototypes of different classes through deploying a contraction procedure in the network, therefore, improving its generalization. Compared with other existing methods, the model has the priority of intuition and no parameter tuning. The performance...
Bankruptcy prediction is a hot topic. Traditional methods consist of univariate model and multivariate model such as neural network. However, the NNs can not extract effective rules. Thus, a novel approach was proposed in this paper to extract rules. First, t-test method was used to select 5 features from 55 original features. Second, the rule encoding was constructed. Third, the ant colony algorithm...
This paper presents one of many possibilities of decision theory that can be used in the modelling of the quality of life in a given city in the Czech Republic. Real data sets from citizen questionnaires for the city of Chrudim were analysed, pre-processed, and used in the classification models. Classifier models, on the basis of C5.0, CHAID, C&RT, and C5.0 boosting algorithms were proposed and...
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