Feature selection, semi-supervised learning and multi-label classification are different challenges for machine learning and data mining communities. While other works have addressed each of these problems separately, in this paper we show how they can be addressed together. We propose a unified framework for semi-supervised multi-label feature selection, based on Laplacian score. In particular, we show how to constrain the function of this score, when data are partially labeled and each instance is associated with a set of labels. We transform the labeled part of data into soft constraints and show how to integrate them in a measure of feature relevance, according to the available labels. Experiments on benchmark data sets are provided for validating the proposed approach and comparing it with some other state-of-the-art feature selection methods in a multi-label context.
The Hadith being the second source of legislation after the Holy Qur'an in the religion of Islam, it represents a large body of knowledge in unstructured textual form. The specification of Hadiths makes its automatic exploitation a rather robust and an almost impossible task. To enable different types of computer systems to exploit this knowledge, various researchers used a formal representation of the semantics of Hadith. The widely used semantic representation is ontology defined as concepts and relations extracted from the Hadith in the form of a structure interpretable both by the machine and the human. In this article, we propose an ontology of the Hadith using an approach inspired by the "METHONTOLOGY" methodology. In this project, we are dealing with religious texts in traditional Arabic, and we face many difficulties in achieving complete precision and correctness. Hence, we decided to follow an entirely manual process to ensure the correctness of the results. Since manual ontology development is both time and effort consuming, we decided to focus only on "Wudhu2" related Hadiths.
In spite of the efforts made in the Arabic language on the syntactic and semantic level, it remains very restricted, even those on the Arabic Sacred Book are few and very limited, due to its difficulties and peculiarities. In this paper we tried to shed the light on some of the recent works that have been conducted to present a semantic representation and manipulation of the Islamic texts to define the problems, limitations and the possible future works that need our intention to improve the semantic support in the Arabic religious texts. Furthermore, we intent to briefly present our project that aims to help us reading, understanding, and interpreting the Islamic legislative sources. The goal of this project is divided into two main tasks which are the creation of an ontology representing the Islamic knowledge and the development of a system which can analyze this knowledge. The ultimate goal is to assist the muftis and facilitate their job.
PurposeParkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approachTo provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.FindingsThe proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/valueA novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.
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