Computational complexity analysis of decision tree algorithms

YesDecision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets

Computational complexity analysis of decision tree algorithms

YesDecision tree is a simple but powerful learning technique that is considered as one of the famous learning algorithms that have been successfully used in practice for various classification tasks. They have the advantage of producing a comprehensible classification model with satisfactory accuracy levels in several application domains. In recent years, the volume of data available for learning is dramatically increasing. As a result, many application domains are faced with a large amount of data thereby posing a major bottleneck on the computability of learning techniques. There are different implementations of the decision tree using different techniques. In this paper, we theoretically and experimentally study and compare the computational power of the most common classical top-down decision tree algorithms (C4.5 and CART). This work can serve as part of review work to analyse the computational complexity of the existing decision tree classifier algorithm to gain understanding of the operational steps with the aim of optimizing the learning algorithm for large datasets

Deterministic Classifiers Accuracy Optimization for Cancer Microarray Data

The objective of this study was to improve classification accuracy in cancer microarray gene expression data using a collection of machine learning algorithms available in WEKA. State of the art deterministic classification methods, such as: Kernel Logistic Regression, Support Vector Machine, Stochastic Gradient Descent and Logistic Model Trees were applied on publicly available cancer microarray datasets aiming to discover regularities that provide insights to help characterization and diagnosis correctness on each cancer typology. The implemented models, relying on 10-fold cross-validation, parameterized to enhance accuracy, reached accuracy above 90%. Moreover, although the variety of methodologies, no significant statistic differences were registered between them, at significance level 0.05, confirming that all the selected methods are effective for this type of analysis.info:eu-repo/semantics/publishedVersio

What’s a (Childless) Man Without a Woman? The Differential Importance of Couple Dynamics for the Wellbeing of Childless Men and Women in the Netherlands

Using rich couple data from the Netherlands Kinship Panel Study, we investigated to what extent there were gender differences in couple dynamics within childless couples (N = 163). Though the childless partners reported similar relationship satisfaction, we found gender differences in the link between relationship conflict and relationship satisfaction - the childless men were more strongly affected by the negative aspects of the partnership. This gender difference was not evident for the association between partner support and relationship satisfaction - the positive aspects of the partnership were equally important for the male and the female childless partners. Furthermore, the association between relationship satisfaction and health was stronger for the childless men than for the childless women and this difference was particularly evident when the levels of relationship satisfaction were low. These results indicate that when they are in unsatisfying romantic relationships, childless men are at a greater risk than childless women of physical and mental ill health

Negated bio-events: Analysis and identification

Background: Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations.Results: We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP'09 ST), and have identified the main types of negated bio-events. We have analysed the key aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP'09 ST corpus, and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events.Conclusions: Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The resulting systems will be able to extract bio-events with attached polarities from textual documents, which can serve as the foundation for more elaborate systems that are able to detect mutually contradicting bio-events. © 2013 Nawaz et al.; licensee BioMed Central Ltd

Establishment of a human skeletal muscle-derived cell line : biochemical, cellular and electrophysiological characterization

Excitation-contraction coupling is the physiological mechanism occurring in muscle cells whereby an electrical signal sensed by the dihydropyridine receptor located on the transverse tubules is transformed into a chemical gradient (Ca2+ increase) by activation of the ryanodine receptor located on the sarcoplasmic reticulum membrane. In the present study, we characterized for the first time the excitation-contraction coupling machinery of an immortalized human skeletal muscle cell line. Intracellular Ca2+ measurements showed a normal response to pharmacological activation of the ryanodine receptor, whereas 3D-SIM (super-resolution structured illumination microscopy) revealed a low level of structural organization of ryanodine receptors and dihydropyridine receptors. Interestingly, the expression levels of several transcripts of proteins involved in Ca2+ homoeostasis and differentiation indicate that the cell line has a phenotype closer to that of slow-twitch than fast-twitch muscles. These results point to the potential application of such human muscle-derived cell lines to the study of neuromuscular disorders; in addition, they may serve as a platform for the development of therapeutic strategies aimed at correcting defects in Ca2+ homoeostasis due to mutations in genes involved in Ca2+ regulation