Combining symbolic and numeric techniques for DL contents classification and analysis

Colloque avec actes et comité de lecture. internationale.International audienceThe goal of this article is to prove that the mixture of different classification and mining techniques coming from so different areas such as the numeric and the symbolic worlds can combine their mutual advantages in order to produce a significant enhancement of the overall classification and retrieval performance in a Data Mining or Information Retrieval context

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study

Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 102 publications. Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.Comment: Under submission to Software Testing, Verification, and Reliability journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an earlier study that this study extends

An analysis of the efficiency of ontology and symbolic learning algorithms in indigenous knowledge representation

It is without a doubt that machine learning has been the area of focus in early days of artificial intelligence, but the early neural networks approach suffered some shortcomings and this led to a temporary decline in research capacity. New symbolic learning techniques have emerged since then which have yielded promising results and have led to a revival in research in machine learning. This has seen many researchers focusing on these techniques and experimenting with them by comparing their performances for different applications. With that in mind, the research thus decided to make an analysis of the symbolic approach against other approaches such as the neural network (connectionist) to evaluate the power of the former approach. This was done by first generating an ontology that acted as a representation of some collected indigenous knowledge. It is from this ontology that a dataset was generated. The dataset was made ambiguous to see the learning power of classifiers in such data. Two experiments were done, one using WEKA and the other using Orange as tools. The reason why the two experiments were used is because there was not a single tool which contained all the required learning algorithms. The research wanted to make use of ID3 and CN2 symbolic algorithm. However, WEKA had ID3 and not CN2 while Orange had CN2 and not ID3. The most important attributes from the ontology regarding the indigenous knowledge were the name of the plant, the province it is found and the disease the plant treats. Therefore the dataset had two features which were disease and province and one label which was the name of the plant. The learning algorithm was to use the two features to generate rules used to predict the label. However, there was ambiguity on the dataset. The challenge was that two different labels would contain the same features, thus leading to wrongful classification. This was the core of the research. Even though the learning model concluded this situation as wrongful classification, in real time, a system using the same learning model would provide desired and correct results. The only flow which was there is that the learning model simply used one label to predict under and ignore the other label with similar features. This was identified as a flow for both symbolic and non-symbolic algorithms. There is no way of giving suggestions in the case a user wants a different plant but with similar features. Therefore for classification using an ambiguous dataset, both these approaches proved to have the fore mentions flow. The research then decided to use recall to analyze the power of these approaches. It was discovered that ID3 has better recall than Multilayer perceptron and Naïve Bayes algorithms when using a training set. ID3 managed to recall clearly and effectively three of its classes by a probability of 1 while Bayes Net had only one class with recall probability of 1. To further investigate the issue of recall, cross validation was used to contrast the competence of recall of the three algorithms to strengthen the assertion that indeed ID3 has a better recall as compared to the other two algorithms. Three stages of cross-validation were done, one stage using 10 fold, the other 20 fold, and the last using 50 fold. For all the different stages of crossvalidation, Bayes Net proved to perform better in terms of recall than the other two algorithms. In cross-validation, MLP could recall approximately above 88% of the instances available in contrast to when using training set where the algorithm recall only two out of 18 instances. In overall the symbolic approach proved to be a commendable approach for use over the nonsymbolic approach. The study of machine learning involves the building of learning algorithms, improving upon learning algorithms or making comparisons of machine learning algorithms. The research raised awareness on some improvements that need to be done on not only symbolic algorithms but non-symbolic ones as well. Some improvements include improving on or coming up with algorithms that suggest alternative predictions in cases of ambiguity instead of doing wrongful classification and not reflect on other possibilities

A methodology for the selection of a paradigm of reasoning under uncertainty in expert system development

The aim of this thesis is to develop a methodology for the selection of a paradigm of reasoning under uncertainty for the expert system developer. This is important since practical information on how to select a paradigm of reasoning under uncertainty is not generally available. The thesis explores the role of uncertainty in an expert system and considers the process of reasoning under uncertainty. The possible sources of uncertainty are investigated and prove to be crucial to some aspects of the methodology. A variety of Uncertainty Management Techniques (UMTs) are considered, including numeric, symbolic and hybrid methods. Considerably more information is found in the literature on numeric methods, than the latter two. Methods that have been proposed for comparing UMTs are studied and comparisons reported in the literature are summarised. Again this concentrates on numeric methods, since there is more literature available. The requirements of a methodology for the selection of a UMT are considered. A manual approach to the selection process is developed. The possibility of extending the boundaries of knowledge stored in the expert system by including meta-data to describe the handling of uncertainty in an expert system is then considered. This is followed by suggestions taken from the literature for automating the process of selection. Finally consideration is given to whether the objectives of the research have been met and recommendations are made for the next stage in researching a methodology for the selection of a paradigm of reasoning under uncertainty in expert system development