Induction Algorithm Research Articles

Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms

The fuzzy-rough rule induction algorithms use fuzzy-rough set concepts such as t-norms, implicators and fuzzy tolerance relationship metrics to calculate the upper and lower approximations. In this direction, the paper examines the influence of the novel Webber t-norm on the model performance obtained with the QuickRules and VQRules algorithms over 19 datasets from different research disciplines. The AUC-ROC metric is used to assess model performance as well as the statistical significance compared to the control model with the highest rank. The obtained results revealed that the k-parameter of the Webber t-norm decreases the model descriptive performance as his value increases, but for the predictive performance of the model there was not any influence by this parameter. In both cases, we were able to identify specific algorithm settings, mostly specific metrics for fuzzy tolerance relations that produce models with high accuracy.

Rough set based on least dissimilarity normalized index for handling uncertainty during E-learners learning pattern recognition

The determination of e-learners' learning style in an online environment has raised the potential scope of interest as its exact estimation prompts a sensational improvement in the contents of the learning framework and student performance. It requires a deep investigation of the learning habits of the learner. Grouping e-learners together provides a more quantifiable way to analyze the learner's feedback and log files to discriminate them based on their learning style. This is accomplished with the help of clustering algorithms in data mining that aids in determining their learning styles well. The target clusters are analyzed by generating functional patterns or rules using the rule induction algorithms. Most of the existing works in the literature attributed to the elucidation of learning styles fail to address the uncertainty and inconsistency in the learner's characteristics. The RST is an optimal method for analyzing the learner's behavior in this context. Thus, a Rough set based least dissimilarity normalized index (RS-LDNI) is proposed for resolving uncertainty while estimating e-learners' learning patterns. This RS-LNDI used the merits of Maximum Dependency Attributes (MDA) for categorical clustering such that the maximal dependency between attributes can be determined by splitting attributes instead of Roughness. It also adopted categorical data clustering to attain the correlation between attributes that cannot be used for learning style prediction. The experimental results of the RS-LNDI algorithm outperform the demerits of these existing clustering algorithms by utilizing the reduct and equivalence class property of rough set theory.

Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms

Webber t-norm and its influence on QuickRules and VQRules fuzzy-rough rule induction algorithms

Concise Rule Induction Algorithm Based on One-Sided Maximum Decision Tree Approach

Concise Rule Induction Algorithm Based on One-Sided Maximum Decision Tree Approach

An integrated knowledge-based system for early detection of eye refractive error using data mining

Refractive error is one of optical defect in the human visual system. Refractive error is a very common disease these days in all populations and in all age groups. Uncorrected and undetected refractive error contributes to visual impairment, blindness and places a considerable burden on a person in the world. The long use of technological devices such as smart phones also poses a new burden on the human eye. The intensity and brightness of these digital devices open a new door for high prevalence of eye refractive errors. Early medical diagnosis of the disease may help in avoiding complications and blindness. Data mining algorithms can be applied to help in ophthalmology and detection of an eye disease at an early stage. So mining the ophthalmology data in efficient manner is a critical issue. This research work deals with development of an integrated knowledge-based system that helps to detect eye refractive error early and provides appropriate advice for the patients. In this study, the hybrid knowledge discovery process model of data mining that was developed for academic research is used. About 9000 ophthalmology data from selected eye health centers are used to build the model. The sample data was preprocessed for missing values, outliers, and noise. Then the model is built using decision tree (J48 and REPTree) and rule induction (JRip and part) algorithms. The part algorithm has registered better predictive performance with accuracy of 60% and 96.45% for subjective and objective based model evaluation, respectively as compared to J48, REPTree, and JRip. Finally, the knowledge discovered with this algorithm is further used to build the knowledge-based systems. The Java programing language is used to integrate data mining results to knowledge-based system. The performance of the proposed system is evaluated by preparing test cases. Overall, the knowledge based system resulted in 89.2% accuracy. Finally the study concludes that discovering knowledge using data mining techniques could be used as a functional eye refractive error detection system.

Hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory

This research proposes a hybrid approach for adaptive fuzzy grid partitioning and rule generation using rough set theory to address the problem of customer segmentation based on purchasing behavior. The objective is to minimize the fuzziness of the partitioning while maximizing the accuracy and interpretability of the generated rules. The research utilizes a dataset consisting of customer transactions, including demographics, purchase details, and satisfaction ratings. The fuzzy grid partitioning process divides the customer space into grid cells, representing different segments. Fuzzy membership values are assigned to data points based on their association with each grid cell. Rough set theory is employed for attribute reduction, identifying the most relevant attributes for customer segmentation. Rule induction algorithms generate rules that capture the patterns and dependencies among customer attributes and their association with specific grid cells. The hybrid approach combines the advantages of adaptive fuzzy grid partitioning and rough set-based rule generation. The optimization process adjusts fuzzy membership values and refines the generated rules to improve accuracy and interpretability. A numerical example and a case study in the retail industry are presented to demonstrate the effectiveness of the proposed approach. Results show successful customer segmentation and generation of actionable rules for marketing strategies. The research contributes to the field of customer segmentation by providing a comprehensive methodology that integrates adaptive fuzzy grid partitioning and rule generation using rough set theory. The hybrid approach offers valuable insights into customer behavior, enabling targeted marketing campaigns, personalized recommendations, and enhanced customer satisfaction.

Chaotic binary Group Search Optimizer for feature selection

Feature selection (FS) is recognized as one of the majority public and challenging problems in the Machine Learning domain. FS can be examined as an optimization problem that needs an effective optimizer to determine its optimal subset of more informative features. This paper proposes a wrapper FS method that combines chaotic maps (CMs) and binary Group Search Optimizer (GSO) called CGSO, which is used to solve the FS problem. In this method, five chaotic maps are incorporated with the GSO algorithm’s main procedures, namely, Logistic, Piecewise, Singer, Sinusoidal, and Tent. The GSO algorithm is used as a search strategy, while k-NN is employed as an induction algorithm. The objective function is to integrate three main objectives: maximizing the classification accuracy value, minimizing the number of selected features, and minimizing the complexity of generated k-NN models. To evaluate the proposed methods’ performance, twenty well-known UCI datasets are used and compared with other well-known published methods in the literature. The obtained results reveal the superiority of the proposed methods in outperforming other well-known methods, especially when using binary GSO with Tent CM. Finally, it is a beneficial method to be utilized in systems that require FS pre-processing.

Incident Management for Explainable and Automated Root Cause Analysis in Cloud Data Centers    

Effective root cause analysis (RCA) of performance issues in modern cloud environ- ments remains a hard problem. Traditional RCA tracks complex issues by their signatures known as problem incidents. Common approaches to incident discovery rely mainly on expertise of users who define environment-specific set of alerts and >target detection of problems through their occurrence in the monitoring system. Adequately modeling of all possible problem patterns for nowadays extremely sophisticated data center applications is a very complex task. It may result in alert/event storms including large numbers of non-indicative precautions. Thus, the crucial task for the incident-based RCA is reduction of redundant recommendations by prioritizing those events subject to importance/impact criteria or by deriving their meaningful groupings into separable situations. In this paper, we consider automation of incident discovery based on rule induction algorithms that retrieve conditions directly from monitoring datasets without consuming the sys- tem events. Rule-learning algorithms are very flexible and powerful for many regression and classification problems, with high-level explainability. Since annotated or labeled data sets are mostly unavailable in this area of technology, we discuss data self-labelling principles which allow transforming originally unsupervised learning tasks into classification problems with further application of rule induction methods to incident detection.

Inductive graph invariants and approximation algorithms

We introduce and study an inductively defined analogue [Formula: see text] of any increasing graph invariant [Formula: see text]. An invariant [Formula: see text] is increasing if [Formula: see text] whenever [Formula: see text] is an induced subgraph of [Formula: see text]. This inductive analogue simultaneously generalizes and unifies known notions like degeneracy, inductive independence number, etc., into a single generic notion. For any given increasing [Formula: see text], this gets us several new invariants and many of which are also increasing. It is also shown that [Formula: see text] is the minimum (over all orderings) of a value associated with each ordering. We also explore the possibility of computing [Formula: see text] (and a corresponding optimal vertex ordering) and identify some pairs [Formula: see text] for which [Formula: see text] can be computed efficiently for members of [Formula: see text]. In particular, it includes graphs of bounded [Formula: see text] values. Some specific examples (like the class of chordal graphs) have already been studied extensively. We further extend this new notion by (i) allowing vertex weighted graphs, (ii) allowing [Formula: see text] to take values from a totally ordered universe with a minimum and (iii) allowing the consideration of [Formula: see text]-neighborhoods for arbitrary but fixed [Formula: see text]. Such a generalization is employed in designing efficient approximations of some graph optimization problems. Precisely, we obtain efficient algorithms (by generalizing the known algorithm of Ye and Borodin [Y. Ye and A. Borodin, Elimination graphs, ACM Trans. Algorithms 8(2) (2012) 1–23] for special cases) for approximating optimal weighted induced [Formula: see text]-subgraphs and optimal [Formula: see text]-colorings (for hereditary [Formula: see text]’s) within multiplicative factors of (essentially) [Formula: see text] and [Formula: see text] respectively, where [Formula: see text] denotes the inductive analogue (as defined in this work) of optimal size of an unweighted induced [Formula: see text]-subgraph of the input and [Formula: see text] is the minimum size of a forbidden induced subgraph of [Formula: see text]. Our results generalize the previous result on efficiently approximating maximum independent sets and minimum colorings on graphs of bounded inductive independence number to optimal [Formula: see text]-subgraphs and [Formula: see text]-colorings for arbitrary hereditary classes [Formula: see text]. As a corollary, it is also shown that any maximal [Formula: see text]-subgraph approximates an optimal solution within a factor of [Formula: see text] for unweighted graphs, where [Formula: see text] is maximum size of any induced [Formula: see text]-subgraph in any local neighborhood [Formula: see text].

Simple Robust Grammar Induction with Combinatory Categorial Grammars

We present a simple EM-based grammar induction algorithm for Combinatory Categorial Grammar (CCG) that achieves state-of-the-art performance by relying on a minimal number of very general linguistic principles. Unlike previous work on unsupervised parsing with CCGs, our approach has no prior language-specific knowledge, and discovers all categories automatically. Additionally, unlike other approaches, our grammar remains robust when parsing longer sentences, performing as well as or better than other systems. We believe this is a natural result of using an expressive grammar formalism with an extended domain of locality.

A Study on the Application of Decision Tree Algorithm in Mobile Marketing

The decision tree algorithm is an inductive learning algorithm based on examples. It infers a classification rule represented by a tree structure through the learning of the training set. Mobile telecom operators have accumulated a large amount of user information in their long-term operations. Using decision tree algorithms to perform data mining on user information can accurately analyze user needs and user satisfaction with services. Based on the research of decision tree algorithm, this paper conducts analysis and mining based on mobile user data in order to improve the level of mobile marketing services and implement customer refined management strategies.

RETRACTED ARTICLE: Management of offshore oil pollution and logistics transportation based on decision tree

Decision tree algorithm is one of the most widely used inductive thinking algorithms in data mining. Its construction does not require any domain knowledge or parameter settings, and it is widely suitable for exploratory knowledge discovery. The decision tree algorithm has clear structure, fast calculation speed, high precision, and higher flexibility and reliability. It can be used to process multidimensional data, and the acquired knowledge is intuitive and easy to understand. Decision tree algorithms are now widely used in medicine, manufacturing, financial analysis, astronomy, molecular biology, and remote sensing image classification. For example, in the field of marine oil pollution, the influence of bacterial flora has been studied through experiments with simulated bar graphs and bio-enhanced seawater oil pollution remediation, environmental parameters, and microbial metabolism have been found to affect the oil diffusion rate at the same time. In the early days, due to the high concentration of microorganisms, the established bacterial colonies that degraded petroleum had a certain degrading ability. As the abundance of microbial communities increases, the benefits of TCOB-4 and TCOB-5 gradually decrease. The oil of TCOB-5 is highly degradable, and may continue to function during the test period, and is compatible with other oil-degrading bacteria. The dominant position. With the rapid development of third-party logistics, logistics profits, the third source of profits, have become a black hole in the entire supply chain. If transportation costs can be reduced, the efficiency of the logistics system can be improved and profits can be maximized. Therefore, studying the decisions of third-party logistics transportation providers has undoubtedly become the top priority of ensuring fast transportation, reducing transportation costs, and conducting effective logistics management research.

ML-based group method of data handling: an improvement on the conventional GMDH

Machine learning (ML) has been recognized as a feasible and reliable technique for the modeling of multi-parametric datasets. In real applications, there are different relationships with various complexities between sets of inputs and their corresponding outputs. As a result, various models have been developed with different levels of complexity in the input–output relationships. The group method of data handling (GMDH) employs a family of inductive algorithms for computer-based mathematical modeling grounded on a combination of quadratic and higher neurons in a certain number of variable layers. In this method, a vector of input features is mapped to the expected response by creating a multistage nonlinear pattern. Usually, each neuron of the GMDH is considered a quadratic partial function. In this paper, the basic structure of the GMDH technique is adapted by changing the partial functions to enhance the complexity modeling ability. To accomplish this, popular ML models that have shown reasonable function approximation performance, such as support vector regression and random forest, are used, and the basic polynomial functions in the GMDH are replaced by these ML models. The regression feasibility and validity of the ML-based GMDH models are confirmed by computer simulation.

Security investment of interdependent and identical CPSs: Stochastic games with asymmetric information

This paper considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors, where measurements of each sensor are transmitted to the corresponding remote estimator over a shared communication network with interdependent security. A stochastic non-cooperative game framework with asymmetric information is provided, in which each sensor is in pursuit of minimizing the security investment cost on the first stage associated with the expected error covariance as small as possible at the corresponding remote estimator on the second stage. The asymmetry of information among sensors poses a challenge to characterize or compute the Nash equilibria (NE). To overcome the challenge, based on the common information among sensors, the game with asymmetric information is transformed into another game with symmetric information such that a subclass of NE refer to common information based Markov perfect equilibria of the original game can be achieved by using a backward induction algorithm. Finally, a numerical example is presented to verify the obtained results.

Inductive and irregular dynamic network representation based on ordinary differential equations

Dynamic network representation has aroused increasing attention from researchers since it reflects the characteristics of real-world networks and has been widely used in various applications. Enormous efforts have been made to this problem. Existing studies often use fixed-time steps. However, in fact, the dynamics in real-world networks are usually irregular over time. To address this problem, in this paper, we employ ordinary differential equations and propose a novel graph representation approach for dynamic networks, namely Graph-ODE. The core idea lies in that it leverages ordinary differential equations to model the continuous latent dynamics of the network and therefore can naturally capture the evolution pattern. Specifically, to deal with the irregularity of node interactions, we first propose a differential form of GRU aggregator to aggregate the chronological neighbors. In addition, we employ neighbor sampling strategies for efficiency. Besides, our inductive algorithm owns the ability to deal with new emerging nodes. Experimental results on various real-world data sets demonstrate that our method significantly outperforms several state-of-the-art algorithms on downstream tasks.

Multi-Frequency Magnetic Induction Tomography System and Algorithm for Imaging Metallic Objects.

Magnetic induction tomography (MIT) is largely focused on applications in biomedical and industrial process engineering. MIT has a great potential for imaging metallic samples; however, there are fewer developments directed toward the testing and monitoring of metal components. Eddy-current non-destructive testing is well established, showing that corrosion, fatigue and mechanical loading are detectable in metals. Applying the same principles to MIT would provide a useful imaging tool for determining the condition of metal components. A compact MIT instrument is described, including the design aspects and system performance characterisation, assessing dynamic range and signal quality. The image rendering ability is assessed using both external and internal object inclusions. A multi-frequency MIT system has similar capabilities as transient based pulsed eddy current instruments. The forward model for frequency swap multi-frequency is solved, using a computationally efficient numerical modelling with the edge-based finite elements method. The image reconstruction for spectral imaging is done by adaptation of a spectrally correlative base algorithm, providing whole spectrum data for the conductivity or permeability.

Predicting and interpreting oxide glass properties by machine learning using large datasets

With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive performance of three machine learning algorithms for six different glass properties. For such, we used an extensive dataset of about 150,000 oxide glasses, which was segmented into smaller datasets for each property investigated. Using the decision tree induction, k-nearest neighbors, and random forest algorithms, selected from a previous study of six algorithms, we induced predictive models for glass transition temperature, liquidus temperature, elastic modulus, thermal expansion coefficient, refractive index, and Abbe number. Moreover, each model was induced with default and tuned hyperparameter values. We demonstrate that, apart from the elastic modulus (which had the smallest training dataset), the induced predictive models for the other five properties yield a comparable uncertainty to the usual data spread. However, for glasses with extremely low or high values of these properties, the prediction uncertainty is significantly higher. Finally, as expected, glasses containing chemical elements that are poorly represented in the training set yielded higher prediction errors. The method developed here calls attention to the success and possible pitfalls of machine learning algorithms. The analysis of the SHAP values indicated the key elements that increase or decrease the value of the modeled properties. It also estimated the maximum possible increase or decrease. Insights gained by this analysis can help empirical compositional tuning and computer-aided inverse design of glass formulations.

A computational exploration of melodic patterns in Arab-Andalusian music

Here we present a computational approach to identifying melodic patterns in a dataset of 145 MusicXML scores with the aim of contributing to centonization theory in the Moroccan tradition of Arab-Andalusian Music – a theory in development by expert performer and researcher of this tradition, Amin Chaachoo. Central to his work is the definition of a set of characteristic patterns, or centos, for each ṭab‘, or melodic mode. We apply three methods: TF-IDF, Maximally General Distinctive Patterns (MGDP) and the Structure Induction Algorithm (SIA) to identify characteristic patterns at the level of ṭab‘. A substantial number of the centos proposed by Chaachoo are identified and new melodic patterns are retrieved. A discussion with Chaachoo about the obtained results promoted the elicitation of other categories of recurrent patterns in the tradition different from the centos, contributing to a deeper musicological knowledge of the tradition.

Machine Learning based Classification for Heart Disease Identification

Cardiac illness is one of the unpredictable infections and around the world numerous individuals experienced this sickness. On schedule and effective recognizable proof of coronary illness assumes a critical part in medical care, especially in the arena of cardiology. A productive and precise framework is proposed to finding coronary illness and the framework depends on AI procedures. Supervised learning algorithms such as Multi-Layer Perceptron (MLP), Multinomial Logistic Regression (MLR), Fuzzy Unordered Rule Induction Algorithm (FURIA) and C4.5 are then used to model CAD cases. This approach is tested on medical data that has 26 features and 335. MLR accomplishes most noteworthy expectation precision of 88.4 %. This methodology is benchmarked on Cleveland heart coronary illness information also. For this situation additionally, MLR, beats different methods. Projected hybridized model increases the exactness of arrangement calculations from 8.3 % to 11.4 % for the Cleaveland information. The proposed technique is, along these lines, a promising tool for finding CAD patients with improved forecast precision.

Bulk-Deformed Potentials for Toric Fano Surfaces, Wall-Crossing, and Period

Abstract We provide an inductive algorithm to compute the bulk-deformed potentials for toric Fano surfaces via wall-crossing techniques and a tropical–holomorphic correspondence theorem for holomorphic discs. As an application of the correspondence theorem, we also prove a big quantum period theorem for toric Fano surfaces, which relates the log descendant Gromov–Witten invariants with the oscillatory integrals of the bulk-deformed potentials.