Voting Classification Algorithms Research Articles

Low-Power Analog Integrated Architecture of the Voting Classification Algorithm for Diabetes Disease Prediction.

A low-power (∼ 600nW), fully analog integrated architecture for a voting classification algorithm is introduced. It can effectively handle multiple-input features, maintaining exceptional levels of accuracy and with very low power consumption. The proposed architecture is based on a versatile Voting algorithm that selectively incorporates one of three key classification models: Bayes or Centroid, or, the Learning Vector Quantization model; all of which are implemented using Gaussian-likelihood and Euclidean distance function circuits, as well as a current comparison circuit. To evaluate the proposed architecture, a comprehensive comparison with popular analog classifiers is performed, using real-life diabetes dataset. All model architectures were trained using Python and compared with the software-based classifiers. The circuit implementations were performed using the TSMC 90 nm CMOS process technology and the Cadence IC Suite was utilized for the design, schematic and post-layout simulations. The proposed classifiers achieved sensitivity of ≥ 96.7% and specificity of ≥ 89.7%.

A Neural Network-Based Weighted Voting Algorithm for Multi-Target Classification in WSN.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a neural network(NN)-based weighted voting classification algorithm is proposed on the basis of the NN-based classifier and combined with the idea of voting strategy, which is implemented on the nodes of the WSN monitoring system by means of the "upper training, lower transplantation" approach. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of about 85% when utilizing a deep neural network (DNN) and deep belief network (DBN) as the base classifier. The experiment reveals that the NN-based weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single NN-based classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater overhead time to run.

Gene Expression Analysis in Radiotherapy Patients and C57BL/6 Mice as a Measure of Exposure to Ionizing Radiation

Dose assessment after radiological disasters is imperative to decrease mortality through rationally directed medical intervention. Our goal was to identify biomarkers capable of qualitative (nonirradiated/irradiated) and/or quantitative (dose) assessment of radiation exposure. Using real-time quantitative PCR, biodosimetry genes were identified in blood samples from cancer patients undergoing total-body irradiation. Time- (5, 12, 23, 48 h) and dose- (0-8 Gy) dependent changes in gene expression were examined in C57BL/6 mice. A training set was used to derive weighted voting classification algorithms (nonirradiated/irradiated) and continuous regression (dose assessment) models that were tested in a separate validation set of mice. Of eight biodosimetry genes identified in cancer patients ( ACTA2 , BBC3 , CCNG1 , CDKN1A , GADD45A , MDK , SERPINE1 , Tnfrsf10b ), expression of BBC3 , CCNG1 , CDKN1A , SERPINE1 and Tnfrsf10b was significantly (P < 0.05) increased in irradiated mice. CCNG1 and CDKN1A expression segregated irradiated mice from controls with an accuracy, specificity and sensitivity of 96.3, 100.0 and 94.4%, respectively, at 48 h. Multiple linear regression analysis predicted doses for the 0-, 1-, 2-, 4-, 6- and 8-Gy treatment groups as 0.0 ± 0.2, 1.6 ± 1.0, 2.9 ± 1.4, 5.1 ± 2.0, 5.3 ± 0.7 and 10.5 ± 5.6 Gy, respectively. These results suggest that gene expression analysis could be incorporated into biodosimetry protocols for qualitative and quantitative assessment of radiation exposure.

Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study

Background/Aims: Hepatocellular carcinoma (HCC) usually develops following chronic liver inflammation caused by hepatitis C or B virus. Through expression profiling in a rare type of HCC, for which the causes are unknown, we sought to find key genes responsible for each step of hepatocarcinogenesis in the absence of viral influence. Methods: We used 68 non-B, non-C liver tissues (20 HCC, 17 non-tumor, 31 normal liver) for expression profiling with PCR-array carrying 3072 genes known to be expressed in liver tissues. To select the differentially expressed genes, we performed random permutation testing. A weighted voting classification algorithm was used to confirm the reliability of gene selection. We then compared these genes with the results of previous expression profiling studies. Results: A total of 220 differentially expressed genes were selected by random permutation tests. The classification accuracies using these genes were 91.8, 92.0 and 100.0% by a leave-one-out cross-validation, an additional PCR-array dataset and a Stanford DNA microarray dataset, respectively. By comparing our results with previous reports on virus-infected HCC, four genes (ALB, A2M, ECHS1 and IGFBP3) were commonly selected in some studies. Conclusions: The 220 differentially expressed genes selected by PCR-array are potentially responsible for hepatocarcinogenesis in the absence of viral influence.

Texture classification of logged forests in tropical Africa using machine-learning algorithms

This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat Thematic Mapper data. The objective was to increase classification accuracy by applying recently developed algorithms in machine learning that are fast in training. Three voting classification algorithms, Arc-4x, Adaboost and bagging were also tested. Initial results using a decision tree classifier showed that adding selected textural measures increased the accuracy of logged forest classification by almost 40%, although the class accuracy for logged forests was only approximately 50% when using spectral and textural features combined. No further significant increase in the classification of logged forests was obtained by voting classification.

Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms

Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. Therefore, the question of whether one might have to dispense with interpretability in order to keep classification strength is being raised in a growing number of machine learning or data mining papers. The purpose of this paper is to study both theoretically and empirically the problem. First, we provide numerous results giving insight into the hardness of the simplicity-accuracy tradeoff for voting classifiers. Then we provide an efficient ``top-down and prune'' induction heuristic, WIDC, mainly derived from recent results on the weak learning and boosting frameworks. It is to our knowledge the first attempt to build a voting classifier as a base formula using the weak learning framework (the one which was previously highly successful for decision tree induction), and not the strong learning framework (as usual for such classifiers with boosting-like approaches). While it uses a well-known induction scheme previously successful in other classes of concept representations, thus making it easy to implement and compare, WIDC also relies on recent or new results we give about particular cases of boosting known as partition boosting and ranking loss boosting. Experimental results on thirty-one domains, most of which readily available, tend to display the ability of WIDC to produce small, accurate, and interpretable decision committees.

Inducing interpretable voting classifiers without trading accuracy for simplicity

Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previou...

An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and a Naive-Bayes inducer. The purpose of the study is to improve our understanding of why and when these algorithms, which use perturbation, reweighting, and combination techniques, affect classification error. We provide a bias and variance decomposition of the error to show how different methods and variants influence these two terms. This allowed us to determine that Bagging reduced variance of unstable methods, while boosting methods (AdaBoost and Arc-x4) reduced both the bias and variance of unstable methods but increased the variance for Naive-Bayes, which was very stable. We observed that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference. Voting variants, some of which are introduced in this paper, include: pruning versus no pruning, use of probabilistic estimates, weight perturbations (Wagging), and backfitting of data. We found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit. We measure tree sizes and show an interesting positive correlation between the increase in the average tree size in AdaBoost trials and its success in reducing the error. We compare the mean-squared error of voting methods to non-voting methods and show that the voting methods lead to large and significant reductions in the mean-squared errors. Practical problems that arise in implementing boosting algorithms are explored, including numerical instabilities and underflows. We use scatterplots that graphically show how AdaBoost reweights instances, emphasizing not only “hard” areas but also outliers and noise.