Abstract: The objective of the present research manuscript is to
perform parametric, nonparametric, and decision tree analysis to
evaluate two treatments that are being used for breast cancer patients.
Our study is based on utilizing real data which was initially used in
“Tamoxifen with or without breast irradiation in women of 50 years
of age or older with early breast cancer" [1], and the data is supplied
to us by N.A. Ibrahim “Decision tree for competing risks survival
probability in breast cancer study" [2]. We agree upon certain aspects
of our findings with the published results. However, in this
manuscript, we focus on relapse time of breast cancer patients instead
of survival time and parametric analysis instead of semi-parametric
decision tree analysis is applied to provide more precise
recommendations of effectiveness of the two treatments with respect
to reoccurrence of breast cancer.
Abstract: Unified Speech Audio Coding (USAC), the latest MPEG standardization for unified speech and audio coding, uses a speech/audio classification algorithm to distinguish speech and audio segments of the input signal. The quality of the recovered audio can be increased by well-designed orchestra/percussion classification and subsequent processing. However, owing to the shortcoming of the system, introducing an orchestra/percussion classification and modifying subsequent processing can enormously increase the quality of the recovered audio. This paper proposes an orchestra/percussion classification algorithm for the USAC system which only extracts 3 scales of Mel-Frequency Cepstral Coefficients (MFCCs) rather than traditional 13 scales of MFCCs and use Iterative Dichotomiser 3 (ID3) Decision Tree rather than other complex learning method, thus the proposed algorithm has lower computing complexity than most existing algorithms. Considering that frequent changing of attributes may lead to quality loss of the recovered audio signal, this paper also design a modified subsequent process to help the whole classification system reach an accurate rate as high as 97% which is comparable to classical 99%.
Abstract: In recent years, a number of works proposing the
combination of multiple classifiers to produce a single
classification have been reported in remote sensing literature. The
resulting classifier, referred to as an ensemble classifier, is
generally found to be more accurate than any of the individual
classifiers making up the ensemble. As accuracy is the primary
concern, much of the research in the field of land cover
classification is focused on improving classification accuracy. This
study compares the performance of four ensemble approaches
(boosting, bagging, DECORATE and random subspace) with a
univariate decision tree as base classifier. Two training datasets,
one without ant noise and other with 20 percent noise was used to
judge the performance of different ensemble approaches. Results
with noise free data set suggest an improvement of about 4% in
classification accuracy with all ensemble approaches in
comparison to the results provided by univariate decision tree
classifier. Highest classification accuracy of 87.43% was achieved
by boosted decision tree. A comparison of results with noisy data
set suggests that bagging, DECORATE and random subspace
approaches works well with this data whereas the performance of
boosted decision tree degrades and a classification accuracy of
79.7% is achieved which is even lower than that is achieved (i.e.
80.02%) by using unboosted decision tree classifier.
Abstract: It is well known that Logistic Regression is the gold
standard method for predicting clinical outcome, especially
predicting risk of mortality. In this paper, the Decision Tree method
has been proposed to solve specific problems that commonly use
Logistic Regression as a solution. The Biochemistry and
Haematology Outcome Model (BHOM) dataset obtained from
Portsmouth NHS Hospital from 1 January to 31 December 2001 was
divided into four subsets. One subset of training data was used to
generate a model, and the model obtained was then applied to three
testing datasets. The performance of each model from both methods
was then compared using calibration (the χ2 test or chi-test) and
discrimination (area under ROC curve or c-index). The experiment
presented that both methods have reasonable results in the case of the
c-index. However, in some cases the calibration value (χ2) obtained
quite a high result. After conducting experiments and investigating
the advantages and disadvantages of each method, we can conclude
that Decision Trees can be seen as a worthy alternative to Logistic
Regression in the area of Data Mining.
Abstract: Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.