Feature selection in meta learning framework

Feature selection is a key step in data mining. Unfortunately, there is no single feature selection method that is always the best and the data miner usually has to experiment with different methods using a trial and error approach, which can be time consuming and costly especially with very large datasets. Hence, this research aims to develop a meta learning framework that is able to learn about which feature selection methods work best for a given data set. The framework involves obtaining the characteristics of the data and then running alternative feature selection methods to obtain their performance. The characteristics, methods used and their performance provide the examples which are used by a learner to induce the meta knowledge which can then be applied to predict future performance on unseen data sets

Systematic overview of clinical trials of antiarrhythmic drugs

Available from British Library Document Supply Centre-DSC:DXN018073 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Cost-sensitive meta-learning framework

Purpose This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” Design/methodology/approach This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project. Findings The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. Originality/value The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive

Surgery for endometriosis-associated infertility: do we exaggerate the magnitude of effect?

Abstract Objective: Surgery remains the mainstay in the diagnosis and management of endometriosis. The number of surgeries performed for endometriosis worldwide is ever increasing, however do we have evidence for improvement of infertility after the surgery and do we exaggerate the magnitude of effect of surgery when we counsel our patients? The management of patients who failed the surgery could be by repeat surgery or assisted reproduction. What evidence do we have for patients who fail assisted reproduction and what is their best chance for achieving pregnancy? Material and methods: In this study we reviewed the evidence-based practice pertaining to the outcome of surgery assisted infertility associated with endometriosis. Manuscripts published in PubMed and Science Direct as well as the bibliography cited in these articles were reviewed. Patients with peritoneal endometriosis with mild and severe disease were addressed separately. Patients who failed the primary surgery and managed by repeat or assisted reproduction technology were also evaluated. Patients who failed assisted reproduction and managed by surgery were also studied to determine of the best course of action. Results: In patients with minimal and mild pelvic endometriosis, excision or ablation of the peritoneal endometriosis increases the pregnancy rate. In women with severe endometriosis, controlled trials suggested an improvement of pregnancy rate. In women with ovarian endometrioma 4 cm or larger ovarian cystectomy increases the pregnancy rate, decreases the recurrence rate, but is associated with decrease in ovarian reserve. In patients who have failed the primary surgery, assisted reproduction appears to be significantly more effective than repeat surgery. In patients who failed assisted reproduction, the management remains to be extremely controversial. Surgery in expert hands might result in significant improvement in pregnancy rate. Conclusion: In women with minimal and mild endometriosis, surgical excision or ablation of endometriosis is recommended as first line with doubling the pregnancy rate. In patients with moderate and severe endometriosis, surgical excision also is recommended as first line. In patients who failed to conceive spontaneously after surgery, assisted reproduction is more effective than repeat surgery. Following surgery, the ovarian reserve may be reduced as determined by Anti Mullerian Hormone. The antral follicle count is not significantly reduced. In women with large endometriomas > 4 cm the ovarian endometrioma should be removed. In women who have failed assisted reproduction, further management remains controversial in the present time