The paper presents the support vector machine binary decision tree scheme (SVM-BDT) used for broadcast news (BN) audio classification. The SVM-BDT architecture was designed to solve multi-class discrimination problem ...
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ISBN:
(纸本)9789537044138
The paper presents the support vector machine binary decision tree scheme (SVM-BDT) used for broadcast news (BN) audio classification. The SVM-BDT architecture was designed to solve multi-class discrimination problem of considered acoustic events: pure speech, speech with music, speech with environment sound, music, and environment sound. Its performance was investigated by using Mel-frequency cepstral coefficients (MFCCs), as a powerful signal parameterization technique, for each SVM binary classifier. The one-against-all strategy in combination with Euclidean distance algorithm was implemented in discrimination process, in order to decrease the influence of missclassification between each class.
A new classification algorithm for mufti-image classification in genetic programming (GP) is introduced, which is the centered dynamic class boundary determination with quick-decreasing power value of arithmetic progr...
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ISBN:
(纸本)9781424418220
A new classification algorithm for mufti-image classification in genetic programming (GP) is introduced, which is the centered dynamic class boundary determination with quick-decreasing power value of arithmetic progression. In the classifier learning process using GP for mufti-image classification, different sets of power values are tested to achieve a more suitable range of margin values for the improvement of the accuracy of the classifiers. In the second development, the program size is introduced into the fitness function to control the size of program growth during the evolutionary learning process. The approach is examined on a Chinese character image data set and a grass leaves data set, both of which have four or more classes. The experimental results show that while dealing with complicated problems of mufti-image classification, the new approach can he used for more accurate classification and work better than the previous algorithms of either static or dynamic class boundary determination. With the fitness function, the size of the programs in the population can be controlled effectively and shortened considerably during evolution. Thus, the readability of the programs could be seemingly improved.
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature l...
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ISBN:
(纸本)9780769537450
In this paper, a general decision layer classification fusion model, based on information fusion for improving classification precision, is proposed, that is, different multi-classification algorithms as the feature layer doing respective classification, and the results of classification algorithms are Input into decision level, the last classification result is output. This model is applied into improving precision of text classification. And the model is used to the Computer Center of some department. Through the experiment, the text classification fusion model can improve the classification precision effectively
Heart is the most vital organ which circulates blood along with nutrients and oxygen throughout the body. There are number of reasons which may affect its normal working. In this paper ten heart diseases, as well as n...
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ISBN:
(纸本)9781479988907
Heart is the most vital organ which circulates blood along with nutrients and oxygen throughout the body. There are number of reasons which may affect its normal working. In this paper ten heart diseases, as well as normal, have been classified by extracting features from original ECG (electrocardiogram) signals and sixth level wavelet transformed ECG signals. The results have been compared and improved accuracy has been obtained using wavelet transformed signals.
Due to the problem of high-dimensionality (datasets which contain many independent attributes or features), feature selection has become an important part of data mining research. One popular form of feature selection...
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ISBN:
(纸本)9781479929719
Due to the problem of high-dimensionality (datasets which contain many independent attributes or features), feature selection has become an important part of data mining research. One popular form of feature selection, wrapper selection, chooses the best features by directly addressing the question of which features build the best models. Various feature subsets are used to build classification models, and the performance of these models is the score of each feature subset. The feature subset with the best score is then used to build the final classification model. As wrappers use a classification algorithm (learner) both to select the features and to build a predictive model, it has been traditional to use the same learner for both, such that the features chosen will be those which optimize that model's performance. However, no research has considered whether having different learners operate inside and outside the wrapper (that is, for selecting the features and for building the final model) might actually result in improved classification performance. In this work, we consider five learners both inside the wrapper and for building the classification model, along with two datasets drawn from the domain of Twitter profile mining. By considering both the raw performance values and a statistical analysis, we find that contrary to intuition, usually the best performance for a given choice of external learner is not found by using the same learner within the wrapper. Instead, the Naive Bayes learner is usually the best choice for selecting features, regardless of which learner is used for the external model. We also find that Multi-Layer Perceptron is able to build consistent classification models for many different choices of internal learner. Finally, the 5-Nearest Neighbor learner gave poor results both inside and outside the wrapper.
The fraud identification area is distinguished by a classify the data (also known as fraud/legitimate) which are not representative general of the population due to sampling bias. To deal with unsupervised fraud detec...
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In microarray data analysis, dimension reduction is an important consideration in the construction of a successful classification algorithm. As an alternative to feature selection, we use a well-known matrix factorisa...
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ISBN:
(纸本)9781424483075
In microarray data analysis, dimension reduction is an important consideration in the construction of a successful classification algorithm. As an alternative to feature selection, we use a well-known matrix factorisation method. For example, we can employ the popular singular-value decomposition (SVD) or nonnegative matrix factorization. In this paper, we consider a novel algorithm for gradient-based matrix factorisation (GMF). We compare GMF and SVD in their application to five gene expression datasets. The experimental results show that our method is faster, more stable, and sensitive.
Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between ...
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ISBN:
(纸本)9783031059360;9783031059353
Correlation is an important information resource, which is often used as a fundamental quantity for modeling tasks in machine learning. Since correlation between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called Quantum Entanglement inspired the classification algorithm (QECA). Particularly, we use the joint probability derived from entangled systems to model correlation between features and categories, that is, Quantum Correlation (QC), and leverage it to develop a novel QC-induced Multi-layer Perceptron framework for classification tasks. Experimental results on four datasets from diverse domains show that QECA is significantly better than the baseline methods, which demonstrates that QC revealed by entangled systems can improve the classification performance of traditional algorithms.
classification is an important research topic in the field of image data mining. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough se...
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ISBN:
(纸本)9780769538761
classification is an important research topic in the field of image data mining. There have been many data classification methods studied, including decision-tree method, statistical methods, neural networks, rough sets, etc. This paper proposed a method to classify the image with normal cloud model which is an uncertainty transformation model between quantities and qualities conception. We develop the algorithm for classification based on normal cloud model. Finally we perform experiments on an artificial trademark image database. The results show the advantages of the cloud model in the process of classification.
This paper uses pulsar signal data for data mining, on the basis of exploratory analysis, constructs a variety of classification models, such as Random Forest, SVM, Logical Regression, K-Nearest Neighbor, Naive Bayes,...
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ISBN:
(纸本)9781728160788
This paper uses pulsar signal data for data mining, on the basis of exploratory analysis, constructs a variety of classification models, such as Random Forest, SVM, Logical Regression, K-Nearest Neighbor, Naive Bayes, Decision tree, AdaBoost classifier, GBDT and XGBoost, to classify pulsar candidate samples. It is hoped that valuable suspected pulsar samples can be effectively screened from massive data for further observation and confirmation.
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