A New Method for Learning Decision Trees from Rules

Shaw

et al. 2022

Front. Public Health

Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates.

Section: Proposed Frameworkmentioning

confidence: 99%

Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems

Shaw

et al. 2022

Front. Public Health

“…In this paper, we investigate if the mining pools are detectable using a machine learning, decision tree based approach [1][15] [37]. It has a tree structure: Each branch represents the outcome of the test, and each leaf node represents a class label.…”

Section: Decision Tree Based Classificationmentioning

confidence: 99%

An Analysis of Transaction Handling in Bitcoin

Gebraselase¹,

Helvik²,

Jiang³

2021

Preprint

Bitcoin has become the leading cryptocurrency system, but the limit on its transaction processing capacity has resulted in increased transaction fee and delayed transaction confirmation. As such, it is pertinent to understand and probably predict how transactions are handled by Bitcoin such that a user may adapt the transaction requests and a miner may adjust the block generation strategy and/or the mining pool to join. To this aim, the present paper introduces results from an analysis of transaction handling in Bitcoin.Specifically, the analysis consists of two-part. The first part is an exploratory data analysis revealing key characteristics in Bitcoin transaction handling. The second part is a predictability analysis intended to provide insights on transaction handling such as (i) transaction confirmation time, (ii) block attributes, and (iii) who has created the block. The result shows that some models do reasonably well for (ii), but surprisingly not for (i) or (iii).

“…Studied the effects of both experience and cognitive style on Web searching. [25] 4.0 DECISION TREE Decision trees are widely used in the classification process [26]. Decision trees are powerful and popular tools for classification and prediction.…”

Section: Research Background In Cognitive Stylesmentioning

confidence: 99%

Comparison of Decision Tree Methods in Classification of Researcher’s Cognitive styles in Academic Environment

2015

In today's internet world, providing feedbacks to users based on what they need and their knowledge is essential. Classification is one of the data mining methods used to mine large data. There are several classification techniques used to solve classification problems. In this article, classification techniques are used to classify researchers as "Expert" and "Novice" based on cognitive styles factors in academic settings using several Decision Tree techniques. Decision Tree is the suitable technique to choose for classification in order to categorize researchers as "Expert" and "Novice" because it produces high accuracy. Environment Waikato Knowledge Analysis (WEKA) is an open source tool used for classification. Using WEKA, the Random Forest technique was selected as the best method because it provides accuracy of 92.72728. Based on these studies, most researchers have a better knowledge of their own domain and their problems and show more competencies in their information seeking behavior compared to novice researchers. This is because the "experts" have a clear understanding of their research problems and is more efficient in information searching activities. Classification techniques are implemented as a digital library search engine because it can help researchers to have the best response according to their demand.