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This paper proposes a classification algorithm based on simplified fuzzy rules base combining fuzzy clustering with rough set. Firstly, generates fuzzy rules base using fuzzy clustering from numerical sample dates, and then simplifies the sample attributions using rough set theory, deletes the redundant rules, and gets the simplified fuzzy rules base, in order to make classification decision conveniently...
Decision tree, as an important classification algorithm in data mining, has been successfully applied in many fields. In this paper, based on the analysis of the essential characteristics of decision tree algorithm, we give a leaf criterion for multi-decision values of decision attribute, and establish a mathematical model for the selection for expanded attributes; also we give a concrete model based...
The enlarging volumes of data resources produced in real world makes classification of very large scale data a challenging task. Therefore, parallel process of very large high dimensional data is very important. Hyper-Surface Classification (HSC) is approved to be an effective and efficient classification algorithm to handle two and three dimensional data. Though HSC can be extended to deal with high...
Visible/near infrared spectroscopy (Vis/NIRS) appears to be a rapid and convenient nondestructive technique that can realize the qualitative analysis and quantitative analysis for many agriculture products. In this study, a novel non-destructive pattern recognition method for honey source was developed base on Visible/Near infrared Spectroscopy. The four types of honey, linden, Chinese milk vetch,...
In Wyner-Ziv video coding (WZVC), the majority of the computational complexity has been shifted from encoder to the decoder in comparison to its conventional video coding technologies. This paper presents a low-complexity frame/block motion activity classification algorithm for WZVC. Improved rate-distortion performance is achieved by adjusting the coding mode of key frame according to the motion...
Detection of marine mammals within an influence zone of episodal anthropogenic noise source is critical to insure the safety of the animals. Marine mammal clicks are closely modeled by AM/FM signals. The Teager-Kaiser energy operator followed by a threshold detector provides an effective means of detecting AM/FM signals. Classification of the species generating the click is done by finding the maximum...
Failure of rectifier circuit has the characteristics of latency and complexity, which leads to the difficulty to fault diagnosis for rectifier circuit. A new method of optimizing support vector machine (SVM) by using ant colony optimization algorithm is presented to fault diagnosis for rectifier circuit in the paper. The experimental object is provided and the six ACO-SVM classifiers are developed...
Keystroke dynamics is a set of computer techniques that has been used successfully for many years for authentication mechanisms and masqueraders detection. Classification algorithms have reportedly performed well, but there is room for improvement. As obtaining real intruders keystrokes is a very difficult task, it has been a common practice to use normal users to capture keystroke data in previous...
Evaluating the performance of a classification algorithm critically requires a measure of the degree to which unseen examples have been identified with their correct class labels. In practice, generalizability is frequently estimated by averaging the accuracies obtained on individual cross-validation folds. This procedure, however, is problematic in two ways. First, it does not allow for the derivation...
Most of graph-based methods for semi-supervised learning are transductive, giving predictions for only the unlabeled data in the training set, and not for an arbitrary test point. SLC (Semi-supervised Local Linear Coordinate), which is based on LLC (Local Linear Coordinate) is present here as an inductive method. The mixture of factor analyzers is used to model the raw data set, and the label smoothness...
As an important preprocessing technology in patent knowledge utilization, patent classification should be accurate and efficient. Commonly used feature selection methods and classification algorithms, like information gain (IG) and k nearest neighbors (k-NN) algorithm, are superior in text classification but have some drawbacks in patent classification. In the paper, we focus on patent classification...
The e-commerce goods classification can put the products of similar nature and characteristics into the same cluster, but the huge amount of data of goods will cause classification algorithm in a high degree of complexity. On the premise of not influencing the classification results, how to reduce the size of the original product data, eliminate noise data interference to a certain extent, and increase...
This study introduces a novel classification algorithm for learning and matching sequences in view independent object tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose, color, texture, etc.) of image features that constitute a model for tracked objects. The temporal dimension is taken into account by using k-mean clusters of sequence...
In this paper, we propose a classification algorithm based on the maximum entropy principle. This algorithm finds the most appropriate class-conditional maximum entropy distributions for classification. No prior knowledge about the form of density function for estimating the class conditional density is assumed except that the information is given in the form of expected valued of features. This algorithm...
Support vector machine (SVM) algorithm has shown a good learning ability and generalization ability in classification, regression and forecasting. This paper mainly analyzes the the performance of support vector machine algorithm in the classification problem, including the algorithm in the kernel function selection, parameter optimization, and integration of other algorithms and to deal with multi-classification...
An automatic recognize method of the subcarrier modulation signals is presented in this paper. The specific model of subcarrier modulation signals is described. The features based on the model are introduced. The classification algorithm using the features is analyzed by the simulation and actual signals. The real-time monitoring system in ultrahigh frequency can utilize the proposed algorithm to...
Facing the drastic market competition and complex environment, the enterprise often meets kinds of crisis, therefore, they need building crisis alert system so as to summarize experience, improve ability of resisting risk and keep themselves develop persistently. This paper gave a classification algorithm by attribute importance (CAAI algorithm). Attributes are reduced by rough set theory, redundant...
This paper presents a new classification algorithm on traffic state of expressway which integrates the ensemble learning and fuzzy system, which consists of two fuzzy classifiers and a speed-based classifier. The fuzzy rules of two fuzzy classifiers are developed based on expert knowledge and how to optimize the parameters in fuzzy classifiers is given. While the outputs of individual classifier are...
Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve classification...
The paper deals with the text classification problem where labeled training samples are very limited while unlabeled data are readily available in large quantities. The paper proposes an efficient classification algorithm that incorporates a weighted k-means clustering scheme into an Expectation Maximization (EM) process. It aims to balance predictive values between labeled and unlabeled training...
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