scholarly journals An Empirical Investigation Into Deep and Shallow Rule Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Florian Beck ◽  
Johannes Fürnkranz
Keyword(s):  
Learning Algorithm ◽  
State Of The Art ◽  
Rule Learning ◽  
Learning Rule ◽  
Disjunctive Normal Form ◽  
Universal Function ◽  
Point Of View ◽  
Positive Class ◽  
Rule Sets ◽  
Inductive Rule Learning

Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concepts, could be easier to learn, in very much the same way as deep neural networks are able to outperform shallow networks, even though the latter are also universal function approximators. However, there are several non-trivial obstacles that need to be overcome before a sufficiently powerful deep rule learning algorithm could be developed and be compared to the state-of-the-art in inductive rule learning. In this paper, we therefore take a different approach: we empirically compare deep and shallow rule sets that have been optimized with a uniform general mini-batch based optimization algorithm. In our experiments on both artificial and real-world benchmark data, deep rule networks outperformed their shallow counterparts, which we take as an indication that it is worth-while to devote more efforts to learning deep rule structures from data.

scholarly journals On-The-Fly Syntheziser Programming with Fuzzy Rule Learning

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 969
Author(s):  
Iván Paz ◽  
Àngela Nebot ◽  
Francisco Mugica ◽  
Enrique Romero
Keyword(s):  
Real Time ◽  
Cross Validation ◽  
State Of The Art ◽  
Rule Learning ◽  
Fuzzy Rule ◽  
Feature Space ◽  
Maximum Volume ◽  
Time Variations ◽  
Fuzzy Rule Learning ◽  
Inductive Rule Learning

This manuscript explores fuzzy rule learning for sound synthesizer programming within the performative practice known as live coding. In this practice, sound synthesis algorithms are programmed in real time by means of source code. To facilitate this, one possibility is to automatically create variations out of a few synthesizer presets. However, the need for real-time feedback makes existent synthesizer programmers unfeasible to use. In addition, sometimes presets are created mid-performance and as such no benchmarks exist. Inductive rule learning has shown to be effective for creating real-time variations in such a scenario. However, logical IF-THEN rules do not cover the whole feature space. Here, we present an algorithm that extends IF-THEN rules to hyperrectangles, which are used as the cores of membership functions to create a map of the input space. To generalize the rules, the contradictions are solved by a maximum volume heuristics. The user controls the novelty-consistency balance with respect to the input data using the algorithm parameters. The algorithm was evaluated in live performances and by cross-validation using extrinsic-benchmarks and a dataset collected during user tests. The model’s accuracy achieves state-of-the-art results. This, together with the positive criticism received from live coders that tested our methodology, suggests that this is a promising approach.

scholarly journals SUBGROUP DISCOVERY OF THE MODY GENES;

2013 ◽  
Vol 20 (05) ◽  
pp. 644-652
Author(s):  
ATTIYA KANWAL ◽  
SAHAR FAZAL ◽  
SOHAIL ASGHAR ◽  
Muhammad Naeem
Keyword(s):  
Metabolic Disorders ◽  
Learning Algorithm ◽  
Rule Learning ◽  
Classification Rule ◽  
Subgroup Discovery ◽  
Autosomal Dominant Disorder ◽  
Data Set ◽  
Support Set ◽  
Confidence Threshold ◽  
Rule Sets

Background: The pandemic of metabolic disorders is accelerating in the urbanized world posing huge burden to healthand economy. The key pioneer to most of the metabolic disorders is Diabetes Mellitus. A newly discovered form of diabetes is MaturityOnset Diabetes of the Young (MODY). MODY is a monogenic form of diabetes. It is inherited as autosomal dominant disorder. Till to date11 different MODY genes have been reported. Objective: This study aims to discover subgroups from the biological text documentsrelated to these genes in public domain database. Data Source: The data set was obtained from PubMed. Period: September-December,2011. Materials and Methodology: APRIORI-SD subgroup discovery algorithm is used for the task of discovering subgroups. A wellknown association rule learning algorithm APRIORI is first modified into classification rule learning algorithm APRIORI-C. APRIORI-Calgorithm generates the rule from the discretized dataset with the minimum support set to 0.42% with no confidence threshold. Total 580rules are generated at the given support. APRIOIR-C is further modified by making adaptation into APRIORI-SD. Results: Experimentalresults demonstrate that APRIORI discovers the substantially smaller rule sets; each rule has higher support and significance. The rulesthat are obtained by APRIORI-C are ordered by weighted relative accuracy. Conclusion: Only first 66 rules are ordered as they cover therelation between all the 11 MODY genes with each other. These 66 rules are further organized into 11 different subgroups. The evaluationof obtained results from literature shows that APRIORI-SD is a competitive subgroup discovery algorithm. All the association amonggenes proved to be true.

scholarly journals Sustainable Agri-Food Processes and Circular Economy Pathways in a Life Cycle Perspective: State of the Art of Applicative Research

2021 ◽  
Vol 13 (5) ◽  
pp. 2472
Author(s):  
Teodora Stillitano ◽  
Emanuele Spada ◽  
Nathalie Iofrida ◽  
Giacomo Falcone ◽  
Anna Irene De Luca
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Circular Economy ◽  
Social Life ◽  
Food Systems ◽  
State Of The Art ◽  
Terrestrial Ecosystems ◽  
Point Of View ◽  
Social Life Cycle Assessment ◽  
Qualitative Synthesis

This study aims at providing a systematic and critical review on the state of the art of life cycle applications from the circular economy point of view. In particular, the main objective is to understand how researchers adopt life cycle approaches for the measurement of the empirical circular pathways of agri-food systems along with the overall lifespan. To perform the literature review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was considered to conduct a review by qualitative synthesis. Specifically, an evaluation matrix has been set up to gather and synthesize research evidence, by classifying papers according to several integrated criteria. The literature search was carried out employing scientific databases. The findings highlight that 52 case studies out of 84 (62% of the total) use stand-alone life cycle assessment (LCA) to evaluate the benefits/impacts of circular economy (CE) strategies. In contrast, only eight studies (9.5%) deal with the life cycle costing (LCC) approach combined with other analyses while no paper deals with the social life cycle assessment (S-LCA) methodology. Global warming potential, eutrophication (for marine, freshwater, and terrestrial ecosystems), human toxicity, and ecotoxicity results are the most common LCA indicators applied. Only a few articles deal with the CE assessment through specific indicators. We argue that experts in life cycle methodologies must strive to adopt some key elements to ensure that the results obtained fit perfectly with the measurements of circularity and that these can even be largely based on a common basis.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

scholarly journals Unsupervised learning by competing hidden units

2019 ◽  
Vol 116 (16) ◽  
pp. 7723-7731 ◽  
Author(s):  
Dmitry Krotov ◽  
John J. Hopfield
Keyword(s):  
Learning Algorithm ◽  
Lower Layer ◽  
Learning Rule ◽  
Backpropagation Algorithm ◽  
Feedforward Networks ◽  
Feature Detectors ◽  
End To End ◽  
Hidden Layer ◽  
Full Network ◽  
Global Inhibition

It is widely believed that end-to-end training with the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility and which is motivated by Hebb’s idea that change of the synapse strength should be local—i.e., should depend only on the activities of the pre- and postsynaptic neurons. We design a learning algorithm that utilizes global inhibition in the hidden layer and is capable of learning early feature detectors in a completely unsupervised way. These learned lower-layer feature detectors can be used to train higher-layer weights in a usual supervised way so that the performance of the full network is comparable to the performance of standard feedforward networks trained end-to-end with a backpropagation algorithm on simple tasks.

New Trends in Asymptotic Approaches: Summation and Interpolation Methods

2001 ◽  
Vol 54 (1) ◽  
pp. 69-92 ◽  
Author(s):  
Igor V. Andrianov ◽  
Jan Awrejcewicz
Keyword(s):  
Applied Sciences ◽  
Solid Mechanics ◽  
State Of The Art ◽  
Asymptotic Methods ◽  
Perturbation Series ◽  
Review Article ◽  
Point Of View ◽  
Advantages And Disadvantages ◽  
New Methods ◽  
Local Character

In this review article, we present in some detail new trends in application of asymptotic techniques to mechanical problems. First we consider the various methods which allows for the possibility of extending the perturbation series application space and hence omiting their local character. While applying the asymptotic methods very often the following situation appears: an existence of the asymptotics ε → 0 implies an existence of the asymptotics ε → ∞ (or, in a more general sense, ε → a and ε → b). Therefore, an idea of constructing a single solution valid for a whole interval of parameter ε changes is very attractive. In other words, we discuss a problem of asymptotically equivalent function constructions possessing for ε → a and ε → b a known asymptotic behavior. The defined problems are very important from the point of view of both theoretical and applied sciences. In this work, we review the state-of-the-art, by presenting the existing methods and by pointing out their advantages and disadvantages, as well as the fields of their applications. In addition, some new methods are also proposed. The methods are demonstrated on a wide variety of static and dynamic solid mechanics problems and some others involving fluid mechanics. This review article contains 340 references.

The smallest extraction problem

2021 ◽  
Vol 14 (11) ◽  
pp. 2445-2458
Author(s):  
Valerio Cetorelli ◽  
Paolo Atzeni ◽  
Valter Crescenzi ◽  
Franco Milicchio
Keyword(s):  
Unsupervised Learning ◽  
Optimization Problem ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Data Extraction ◽  
Source Code ◽  
Web Data ◽  
Web Data Extraction ◽  
New Family ◽  
Context Free

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.

scholarly journals Imbalanced Learning Based on Logistic Discrimination

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Huaping Guo ◽  
Weimei Zhi ◽  
Hongbing Liu ◽  
Mingliang Xu
Keyword(s):  
Statistical Model ◽  
Cost Function ◽  
State Of The Art ◽  
Class Imbalance ◽  
Imbalanced Learning ◽  
Learning Problem ◽  
Logistic Discrimination ◽  
Positive Class ◽  
Negative Class ◽  
Novel Method

In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its performance. To fully consider the class imbalance, we design a new cost function which takes into account the accuracies of both positive class and negative class as well as the precision of positive class. Unlike traditional logistic discrimination, the proposed method learns its parameters by maximizing the proposed cost function. Experimental results show that, compared with other state-of-the-art methods, the proposed one shows significantly better performance on measures of recall,g-mean,f-measure, AUC, and accuracy.

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