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In ubiquitous environment, too much information is generated from a lot of sensors, and people want to obtain the appropriately classified information from the information. Decision tree algorithm like C4.5 is much useful in the field of data mining or machine learning system. Because this is fast and deduces good result on the problem of classification. This paper proposes three methods using decision...
In ubiquitous environment, too much information is generated from a lot of sensors, and people want to obtain the appropriately classified information from the information. Decision tree algorithm like C4.5 is very useful in the field of data mining or machine learning system. Because this is fast and deduces good result on the problem of classification. This paper proposes three methods using decision...
Biological early warning systems (BEWS) which detects toxicity by tracking the physiologic responses of the whole organisms has been developed in recent years. In the paper, we firstly apply the C4.5 decision tree Algorithm to the biological early warning systems and propose a new BEWS which can detect toxicity by classifying the flea's behavior data. The new system is meaningful to both biological...
Classification, which is the task of assigning object (event) to one of several predefined classes, is an important problem in the field of machine learning and data mining. Boosting based technique use deterministic results of weak classifiers to compound them to a strong classifier while classifier can give class distribution as result. It involves losses of information. This problem can be solved...
This paper presents a method of visualizing a multi-dimensional data set into a lower dimensional space, especially into a two-dimensional space, so that people can intuitively conceive the relations or the distance between the entities of the data. Kullback-Leibler divergence is used as the measure to evaluate the distance between the vectors of the probability distribution. The measured distance...
Smart environments is a technological concept that, according to Mark Weiser is ldquoa physical world that is richly and invisibly interwoven with sensors, actuators, displays, and computational elements, embedded seamlessly in the everyday objects of our lives, and connected through a continuous network.rdquo But sometimes the data gathered from the sensors is with different importance. It means...
Biological early warning systems(BEWS) has been developed in recent years. BEWS detects toxicity by tracking the physiologic responses of the whole organisms. In the paper, we apply the classification technique to the biological early warning systems and propose a new BEWS which is meaningful to biological field. Meanwhile, how to select the features in such classification application is also a contribution...
Bootstrap technique has been successfully used in many signal processing systems for data classification. Some of such systems are based on ensemble-based algorithms. These algorithms use multiple classifiers, generally to improve classification performance: each classifier provides an alternative decision whose combination may provide a superior solution than the one provided by any single classifier...
Fingerprint identification is a technology which has been widely accepted for personal identification in many areas such as criminal investigation, access control, and Internet authentication due to its uniqueness. Most available systems for fingerprint identification use the minutiae matching for identification. The performance of minutiae extraction algorithms relies heavily on the quality of the...
Classification of the numerical data is a very important research topic in machine learning. But the incomplete data is very common in real world application. And the existence of incomplete data degrades the learning quality of classification models. But the existence of incomplete data always decrease the quality of classification models, To show the definition of missing data more intuitively,...
The incomplete data in which certain features are missing from particular feature vectors, exists in a wide range of fields, including clustering, computer vision, biological systems, classification systems, remote sensing and so on. Classification is a very important research topic in machine learning. There are many algorithms for classification of the numerical data. But the existence of incomplete...
The rule refinement problem has been known to be one of the most difficult and complex problems. This paper presents a systematic rule refinement method that deals with the old rule directly with the new data, for the first time. To be able to do the rule refinement, the data are represented in the extended data expression, where an event has its weight of importance. To show how this can be done...
We introduce a classification algorithm which can be applied to a problem with a data set included a missing variable. In this algorithm we use data expansion treating it with a weight value and the probability techniques. It is applied to extending a classifier which is considered the optimal projection plane based on Fisher's formula. For doing this, we derive equations from the procedure to be...
In the classical clustering, an item must entirely belong to a cluster. Fuzzy clustering, however, describes more accurately the ambiguous type of structure in data. Fuzzy clustering is useful for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. In fuzzy clustering, the membership of each datum in each cluster is represented by...
During knowledge acquisition, a new attribute can be added at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set with the newly added attribute(s) using the existing algorithms. In this paper, we propose further development of the new inference engine, UChoo, that can handle the above...
In this paper, we propose a method to visualize the distance among data. It is a method for analyzing the distance relationship among the multi-dimensional data represented by a set of attributes. By this method, one can represent high dimensional data sets into low dimensional coordinates. To achieve the goal Kullback-Leibler divergence measure is used for distance determining process and simulated...
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