Logic learning machine

Technopedia Center

Faculty of Engineering and Computer Science
S1 Informatics S1 Information Systems S1 Information Technology S1 Computer Engineering S1 Electrical Engineering S1 Civil Engineering

faculty of Economics and Business
S1 Management S1 Accountancy

Faculty of Letters and Educational Sciences
S1 English literature S1 English language education S1 Mathematics education S1 Sports Education

Logic learning machine - Wikipedia

From Wikipedia, the free encyclopedia

This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these messages)

This article may rely excessively on sources too closely associated with the subject, potentially preventing the article from being verifiable and neutral. Please help improve it by replacing them with more appropriate citations to reliable, independent, third-party sources. (June 2015) (Learn how and when to remove this message)

A major contributor to this article appears to have a close connection with its subject. It may require cleanup to comply with Wikipedia's content policies, particularly neutral point of view. Please discuss further on the talk page. (June 2015) (Learn how and when to remove this message)

(Learn how and when to remove this message)

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning
Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection Data cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Deep learning Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Boltzmann machine Restricted GAN Diffusion model SOM Convolutional neural network U-Net LeNet AlexNet DeepDream Neural radiance field Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop RLHF
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Journals and conferences ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
v t e

Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm,^[1] developed by Marco Muselli, Senior Researcher at the Italian National Research Council CNR-IEIIT in Genoa.

LLM has been employed in many different sectors, including the field of medicine (orthopedic patient classification,^[2] DNA micro-array analysis ^[3] and Clinical Decision Support Systems ^[4]), financial services and supply chain management.

History

The Switching Neural Network approach was developed in the 1990s to overcome the drawbacks of the most commonly used machine learning methods. In particular, black box methods, such as multilayer perceptron and support vector machine, had good accuracy but could not provide deep insight into the studied phenomenon. On the other hand, decision trees were able to describe the phenomenon but often lacked accuracy. Switching Neural Networks made use of Boolean algebra to build sets of intelligible rules able to obtain very good performance. In 2014, an efficient version of Switching Neural Network was developed and implemented in the Rulex suite with the name Logic Learning Machine.^[5] Also, an LLM version devoted to regression problems was developed.

General

Like other machine learning methods, LLM uses data to build a model able to perform a good forecast about future behaviors. LLM starts from a table including a target variable (output) and some inputs and generates a set of rules that return the output value $y$ corresponding to a given configuration of inputs. A rule is written in the form:

if premise then consequence

where consequence contains the output value whereas premise includes one or more conditions on the inputs. According to the input type, conditions can have different forms:

for categorical variables the input value must be in a given subset: $x_{1}\in \{A,B,C,...\}$ .
for ordered variables the condition is written as an inequality or an interval: $x_{2}\leq \alpha$ or $\beta \leq x_{3}\leq \gamma$

A possible rule is therefore in the form

x_{1}\in \{A,B,C,...\}

AND

x_{2}\leq \alpha

AND

\beta \leq x_{3}\leq \gamma

then

y={\bar {y}}

Types

According to the output type, different versions of the Logic Learning Machine have been developed:

Logic Learning Machine for classification, when the output is a categorical variable, which can assume values in a finite set
Logic Learning Machine for regression, when the output is an integer or real number.

References

^ Muselli, Marco (2006). "Switching Neural Networks: A new connectionist model for classification" (PDF). WIRN 2005 and NAIS 2005, Lecture Notes on Computer Science. 3931: 23–30.
^ Mordenti, M.; Ferrari, E.; Pedrini, E.; Fabbri, N.; Campanacci, L.; Muselli, M.; Sangiorgi, L. (2013). "Validation of a New Multiple Osteochondromas Classification Through Switching Neural Networks". American Journal of Medical Genetics Part A. 161 (3): 556–560. doi:10.1002/ajmg.a.35819. PMID 23401177. S2CID 23983960.
^ Cangelosi, D.; Muselli, M.; Blengio, F.; Becherini, P.; Versteeg, R.; Conte, M.; Varesio, L. (2013). "Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients". Bits2013. 15 (Suppl 5): S4. doi:10.1186/1471-2105-15-S5-S4. PMC 4095004. PMID 25078098.
^ Parodi, S.; Filiberti, R.; Marroni, P.; Montani, E.; Muselli, M. (2014). "Differential diagnosis of pleural mesothelioma using Logic Learning Machine". Bits2014. 16 (Suppl 9): S3. doi:10.1186/1471-2105-16-S9-S3. PMC 4464205. PMID 26051106.
^ "Rulex: a software for knowledge extraction from data". Italian National Research Council. Retrieved 7 March 2015.

External links

Categories:

Hidden categories: