B. Q. Huang
University College Dublin
Handwriting recognitionMachine learningSupport vector machineData miningLogistic regressionArtificial intelligencePattern recognitionHidden Markov modelPrincipal component analysisHandwritingService (business)Speech recognitionSymbol recognitionTelecommunications serviceField (computer science)Computer scienceNaive Bayes classifierMedicineTelecommunicationsDecision treeClassifier (UML)
8Publications
5H-index
201Citations
Publications 9
Newest
#1S. Mohamed (UCD: University College Dublin)
#2B. Q. Huang (UCD: University College Dublin)H-Index: 5
Last. M-T. Kechadi (UCD: University College Dublin)H-Index: 4
view all 3 authors...
NB-UVB Phototherapy is one of the most common treatments administrated by dermatologists for psoriasis patients. Although in general, the treatment results in improving the condition, it also can worsen it. If a model can predict the treatment response before hand, the dermatologists can adjust the treatment accordingly. In this paper, we use data mining techniques and conduct four experiments. The best performance of all four experiments was obtained by the stacked classifier made of hyper para...
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#1S. Mohamed (UCD: University College Dublin)
#2B. Q. Huang (UCD: University College Dublin)H-Index: 5
Last. M.-T. Kechadi (UCD: University College Dublin)
view all 3 authors...
The prediction of short term adverse events occurrence in phototherapy treatment is important for the dermatologists who administrate phototherapy to adjust the treatment and standardize the clinical outcomes. Recently, a modeling technique which can detect the potential short term adverse events occurrence in phototherapy treatments is required for clinicians. Based on data mining, this study tends to explore the significant features and the class distribution of training data for the short ter...
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#1B. Q. Huang (UCD: University College Dublin)H-Index: 5
#2Mohand Tahar Kechadi (UCD: University College Dublin)H-Index: 5
Last. Brian Buckley (UCD: University College Dublin)H-Index: 1
view all 3 authors...
This paper presents a new set of features for land-line customer churn prediction, including 2 six-month Henley segmentation, precise 4-month call details, line information, bill and payment information, account information, demographic profiles, service orders, complain information, etc. Then the seven prediction techniques (Logistic Regressions, Linear Classifications, Naive Bayes, Decision Trees, Multilayer Perceptron Neural Networks, Support Vector Machines and the Evolutionary Data Mining A...
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Nov 19, 2010 in ADMA (Advanced Data Mining and Applications)
#1T. Sato (UCD: University College Dublin)H-Index: 1
#2B. Q. Huang (UCD: University College Dublin)H-Index: 5
Last. B. BuckleyH-Index: 1
view all 5 authors...
Failure to identify potential churners affects significantly a company revenues and services that can provide. Imbalance distribution of instances between churners and non-churners and the size of customer dataset are the concerns when building a churn prediction model. This paper presents a local PCA classifier approach to avoid these problems by comparing eigenvalues of the best principal component. The experiments were carried out on a large real-world Telecommunication dataset and assessed o...
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Nov 19, 2010 in ADMA (Advanced Data Mining and Applications)
#1B. Q. Huang (UCD: University College Dublin)H-Index: 5
#2T. Satoh (UCD: University College Dublin)H-Index: 1
Last. B. Buckley (UCD: University College Dublin)H-Index: 2
view all 5 authors...
Imbalance distribution of samples between churners and nonchurners can hugely affect churn prediction results in telecommunication services field. One method to solve this is over-sampling approach by PCA regression. However, PCA regression may not generate good churn samples if a dataset is nonlinear discriminant. We employed Genetic K-means Algorithm to cluster a dataset to find locally optimum small dataset to overcome the problem. The experiments were carried out on a real-world telecommunic...
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Dec 1, 2009 in AusDM (Australasian Data Mining Conference)
#1Takeshi Sato (UCD: University College Dublin)H-Index: 1
#2B. Q. Huang (UCD: University College Dublin)H-Index: 5
Last. B. BuckleyH-Index: 2
view all 5 authors...
Linear Principal Components Analysis (LPCA) is known for its simplicity to reduce the features dimensionality. An extension of LPCA, Kernel Principal Components Analysis (KPCA), outperforms LPCA when applied on non-linear data in high dimensional feature space. However, on large datasets with high input space, KPCA deals with a memory issue and imbalance classification problems with difficulty. This paper presents an approach to reduce the complexity of the training process of KPCA by condensing...
Aug 30, 2009 in DaWaK (Data Warehousing and Knowledge Discovery)
#1B. Q. Huang (UCD: University College Dublin)H-Index: 5
#2M-T. Kechadi (UCD: University College Dublin)H-Index: 4
Last. B. BuckleyH-Index: 2
view all 3 authors...
Although churn prediction has been an area of research in the voice branch of telecommunications services, more focused studies on the huge growth area of Broadband Internet services are limited. Therefore, this paper presents a new set of features for broadband Internet customer churn prediction, based on Henley segments, the broadband usage, dial types, the spend of dial-up, line-information, bill and payment information, account information. Then the four prediction techniques (Logistic Regre...
Source
#1B. Q. Huang (UCD: University College Dublin)H-Index: 5
#3M-T. Kechadi (UCD: University College Dublin)H-Index: 4
This paper proposes a new preprocessing technique for online handwriting. The approach is to first remove the hooks of the strokes by using changed-angle threshold with length threshold, then filter the noise by using a smoothing technique, which is the combination of the cubic spline and the equal-interpolation methods. Finally, the handwriting is normalised. Experiments are carried out using the benchmark UNIPEN database. The experimental results show that our preprocessing technique can impro...
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#1B. Q. Huang (UCD: University College Dublin)H-Index: 5
#4M-T. Kechadi (UCD: University College Dublin)H-Index: 4
This paper presents a combined approach for online handwriting symbols recognition. The basic idea of this approach is to employ a set of left-right HMMs as a feature extractor to produce HMM features, and combine them with global features into a new feature vector as input, and then use SVM as a classifier to finally identify unknown symbols. The new feature vector consists of the global features and several pairs of maximum probabilities with their associated different model labels. A recognis...
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