Inductive Learning Algorithm Research Articles

Formal Software Architecture Rule Learning: A Comparative Investigation between Large Language Models and Inductive Techniques

This paper explores the application of inferring software architecture rules from examples using Machine Learning (ML). We investigate different methods from Inductive Rule Learning and utilize Large Language Models (LLMs). Traditional manual rule specification approaches are time-consuming and error-prone, motivating the need for automated rule discovery. Leveraging a dataset of software architecture instances and a meta-model capturing implementation facts, we used inductive learning algorithms and LLMs to extract meaningful rules. The induced rules are evaluated against a predefined hypothesis and their generalizability across different system subsets is investigated. The research highlights the capabilities and limitations of ML-based rule learning in the area of software architecture, aiming to inspire further innovation in data-driven rule discovery for more intelligent software architecture practices.

Adaptive Ontology-Based IoT Resource Provisioning in Computing Systems

The eagle expresses of cloud computing plays a pivotal role in the development of technology. The aim is to solve in such a way that it will provide an optimized solution. The key role of allocating these efficient resources and making the algorithms for its time and cost optimization. The approach of the research is based on the rough set theory RST. RST is a great method for making a large difference in qualitative analysis situations. It's a technique to find knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, and data reduction. The rough set theory is the new method in cloud service selection so that the best services provide for cloud users and efficient service improvement for cloud providers. The simulation of the work is finished at intervals with the merchandise utilized for the formation of the philosophy framework. The simulation shows the IoT services provided by the IoT service supplier to the user are the best utilization with the parameters and ontology technique.

FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data

AbstractFOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data. It generates an (explainable) answer set programming (ASP) rule set for multi-category classification tasks while maintaining efficiency and scalability. The FOLD-RM algorithm is competitive in performance with the widely used, state-of-the-art algorithms such as XGBoost and multi-layer perceptrons, however, unlike these algorithms, the FOLD-RM algorithm produces an explainable model. FOLD-RM outperforms XGBoost on some datasets, particularly large ones. FOLD-RM also provides human-friendly explanations for predictions.

Heterogeneous data integration methods for patient similarity networks.

Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.

Evaluation of Accounting Data of Water Company Based on Combination Model

Driven by the dual effects of artificial intelligence and accounting transformation, it has become one of the research hotspots of accounting intelligence in China to replace accountants with computers to make professional judgments and automatically evaluate accounting. On the basis of summarizing and analyzing the research status in the field of intelligent accounting, the realization path of intelligent accounting in water companies is put forward in this paper. By introducing the improved Apriori learning algorithm based on Boolean mapping matrix, intelligent data mining is carried out for evaluation of water accounting. With the help of the improved attribute inductive learning algorithm, the corresponding relationship between the original water consumption attribute and the water right transaction is mined and output. In addition, with the help of forward reasoning in inference engine technology, the computer intelligent data evaluation function is realized. Finally, the functional architecture of accounting system of the water company is designed, which provides reference for the development and application of intelligent accounting system, and explores a new path for the integration of accounting and intelligent algorithms.

Learning the travelling salesperson problem requires rethinking generalization

End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes. While state-of-the-art learning-driven approaches for TSP perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances at practical scales. This work presents an end-to-end neural combinatorial optimization pipeline that unifies several recent papers in order to identify the inductive biases, model architectures and learning algorithms that promote generalization to instances larger than those seen in training. Our controlled experiments provide the first principled investigation into such zero-shot generalization, revealing that extrapolating beyond training data requires rethinking the neural combinatorial optimization pipeline, from network layers and learning paradigms to evaluation protocols. Additionally, we analyze recent advances in deep learning for routing problems through the lens of our pipeline and provide new directions to stimulate future research.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039kcal/mol/atom for energy and a root mean square error of 2.57$\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Inductive Graph Representation Learning for fraud detection

Graphs can be seen as a universal language to describe and model a diverse set of complex systems and data structures. However, efficiently extracting topological information from dynamic graphs is not a straightforward task. Previous works have explored a variety of inductive graph representation learning frameworks, but despite the surge in development, little research deployed these techniques for real-life applications. Most earlier studies are restricted to a set of benchmark experiments, rendering their practical generalisability questionable. This paper evaluates the proclaimed predictive performance of state-of-the-art inductive graph representation learning algorithms on highly imbalanced credit card transaction networks. More specifically, we assess the inductive capability of GraphSAGE and Fast Inductive Graph Representation Learning in a fraud detection setting. Credit card transaction fraud networks pose two crucial challenges for graph representation learners: First, these networks are highly dynamic, continuously encountering new transactions. Second, they are heavily imbalanced, with only a small fraction of transactions labelled as fraudulent. This paper contributes to the literature by (i) proving how inductive graph representation learning techniques can be leveraged to enhance predictive performance for fraud detection and (ii) demonstrating the benefit of graph-level undersampling for representation learning in imbalanced networks.

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.

An automatic detection system of diabetic retinopathy using a hybrid inductive machine learning algorithm

Recently, the leading cause of preventable blindness is diabetic retinopathy (DR). Although there are several undiagnosed and non-treated cases of DR, accurate and adequate retinal screening could facilitate the early detection and treatment of DR. The goal of this research is to develop a reliable DR screening and detection model to reduce the risk of DR-related blindness. DR-infected eyes describe ophthalmologist for further examination and diagnosis might reduce the risk of vision loss and provide timely and accurate diagnostic information. Hence, this paper proposes a hybrid inductive machine learning algorithm (HIMLA) as an automated DR detection diagnostic tool. HIMLA processes and classifies colored fundus images as healthy (no retinopathy) or unhealthy (presence of DR) by identifying the appropriate medical DR cases. The proposed algorithm comprises four stages: pre-processing, segmentation, feature extraction, and classification. At the pre-processing stage, colored fundus images are normalized to a specific brightness level to enhance the quality of the images. In the segmentation stage, the processed image is encoded and decoded to segment the images for improving image quality. Furthermore, feature extraction and classification are performed using multiple instance learning (MIL). The proposed method was evaluated on CHASE datasets for the detection of DR. The accuracy, sensitivity, and specificity of the proposed approach are 96.62%, 95.31%, and 96.88%, respectively. These results indicates that HIMLA outperforms other DR models, such as ML-based neovascularization detection in the optic disc (MLB-NVD), genetic algorithm–based diabetic retinopathy (GAB-DR), DL algorithm diabetic retinopathy (DLA-DR), and diagnostic assessment–based DL for diabetic retinopathy (DAD-DR ), which reduces the risk of vision loss.

Joint auto-weighted graph fusion and scalable semi-supervised learning

Graph carries out a key role in graph-based semi-supervised label propagation, as it clarifies the structure of the data manifold. The performance of label propagation methods depends on the adopted graph and can be enhanced by merging different graphs that are obtained from multiple sources of information. While there exist algorithms that perform graph fusion they have several weaknesses. Most of these algorithms define graph fusion and label propagation as two separate tasks. Moreover, when the number of data expands, these strategies are not well-suited due to the use of transductive learning in the label propagation phase which makes the label prediction for unseen samples difficult. Furthermore, very few algorithms extract the information contained in the label space. Additionally, most of the graph fusion techniques adopt equal or static weights for different views, which is not the best choice as distinctive features (hence different graphs) contain various information. To overcome these shortcomings, we propose an Auto-weighted Multi-view Semi-Supervised Learning method (AMSSL), which is based on an inductive learning algorithm (i.e., Flexible Manifold Embedding) and profited a projection matrix for predicting the labels of out-of-sample data. The proposed AMSSL method represents a unified framework that dynamically fuses various information obtained from different features and also from the label space and adaptively designates appropriate weights according to the usefulness of each view. Our experimental results on seven small and large image datasets demonstrate the superiority of the proposed method compared to the use of one single feature and other state-of-the-art graph fusion methods.

Incorporating syllabification points into a model of grapheme-to-phoneme conversion

A model to convert a grapheme into a phoneme (G2P) is crucial in the natural language processing area. In general, it is developed using a probabilistic-based data-driven approach and directly applied to a sequence of graphemes with no other information. Important research shows that incorporating information of syllabification point is capable of improving a probabilistic-based English G2P. However, the information should be accurately provided by a perfect orthographic syllabification. Some noises or errors of syllabification significantly reduce the G2P performance. In this paper, incorporation of syllabification points into a probabilistic-based G2P model for Bahasa Indonesia is investigated. This information is important since Bahasa Indonesia is richer than English in terms of syllables. A 5-fold cross-validating on 50 k words shows that the incorporation of syllabification points significantly improves the performance of G2P model, where the phoneme error rate (PER) can be relatively reduced by 10.75%. This PER is much lower than the G2P model based on an inductive learning algorithm. An important contribution of this research is that the proposed G2P model is quite robust to syllabification errors. A syllable error rate (SER) of 2.5% that comes from an orthographic syllabification model just slightly increases the PER of the proposed G2P model from 0.83% to be 0.90%. A higher SER up to 10% just increase the PER to be 1.14%.

Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

Firms currently operate in highly competitive scenarios, where the environmental conditions evolve over time. Many factors intervene simultaneously and their hard-to-interpret interactions throughout the supply chain greatly complicate decision-making. The complexity clearly manifests itself in the field of inventory management, in which determining the optimal replenishment rule often becomes an intractable problem. This paper applies machine learning to help managers understand these complex scenarios and better manage the inventory flow. Building on a dynamic framework, we employ an inductive learning algorithm for setting the most appropriate replenishment policy over time by reacting to the environmental changes. This approach proves to be effective in a three-echelon supply chain where the scenario is defined by seven variables (cost structure, demand variability, three lead times, and two partners’ inventory policy). Considering four alternatives, the algorithm determines the best replenishment rule around 88% of the time. This leads to a noticeable reduction of operating costs against static alternatives. Interestingly, we observe that the nodes are much more sensitive to inventory decisions in the lower echelons than in the upper echelons of the supply chain.

Cross-domain aspect extraction for sentiment analysis: A transductive learning approach

Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze consumer reviews at a high level of detail, where the opinion about each feature of the product or service is considered. ABSA usually explores supervised inductive learning algorithms, which requires intense human effort for the labeling process. In this paper, we investigate Cross-Domain Transfer Learning approaches, in which aspects already labeled in some domains can be used to support the aspect extraction of another domain where there are no labeled aspects. Existing cross-domain transfer learning approaches learn classifiers from labeled aspects in the source domain and then apply these classifiers in the target domain, i.e., two separate stages that may cause inconsistency due to different feature spaces. To overcome this drawback, we present an innovative approach called CD-ALPHN (Cross-Domain Aspect Label Propagation through Heterogeneous Networks). First, we propose a heterogeneous network-based representation that combines different features (labeled aspects, unlabeled aspects, and linguistic features) from source and target domain as nodes in a single network. Second, we propose a label propagation algorithm for aspect extraction from heterogeneous networks, where the linguistic features are used as a bridge for this propagation. Our algorithm is based on a transductive learning process, where we explore both labeled and unlabeled aspects during the label propagation. Experimental results show that the CD-ALPHN outperforms the state-of-the-art methods in scenarios where there is a high-level of inconsistency between the source and target domains — the most common scenario in real-world applications.

Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification.

The wide 3D applications have led to increasing amount of 3D object data, and thus effective 3D object classification technique has become an urgent requirement. One important and challenging task for 3D object classification is how to formulate the 3D data correlation and exploit it. Most of the previous works focus on learning optimal pairwise distance metric for object comparison, which may lose the global correlation among 3D objects. Recently, a transductive hypergraph learning has been investigated for classification, which can jointly explore the correlation among multiple objects, including both the labeled and unlabeled data. Although these methods have shown better performance, they are still limited due to 1) a considerable amount of testing data may not be available in practice and 2) the high computational cost to test new coming data. To handle this problem, considering the multi-modal representations of 3D objects in practice, we propose an inductive multi-hypergraph learning algorithm, which targets on learning an optimal projection for the multi-modal training data. In this method, all the training data are formulated in multi-hypergraph based on the features, and the inductive learning is conducted to learn the projection matrices and the optimal multi-hypergraph combination weights simultaneously. Different from the transductive learning on hypergraph, the high cost training process is off-line, and the testing process is very efficient for the inductive learning on hypergraph. We have conducted experiments on two 3D benchmarks, i.e., the NTU and the ModelNet40 data sets, and compared the proposed algorithm with the state-of-the-art methods and traditional transductive multi-hypergraph learning methods. Experimental results have demonstrated that the proposed method can achieve effective and efficient classification performance. We also note that the proposed method is a general framework and has the potential to be applied in other applications in practice.

Accurate classification of socially generated medical discourse

The growth of online health communities particularly those involving socially generated content can provide considerable value for society. Participants can gain knowledge of medical information or interact with peers on medical forum platforms. Analysing sentiment expressed by members of a health community in medical forum discourse can be of significant value, such as by identifying a particular aspect of an information space, determining themes that predominate among a large data set, and allowing people to summarize topics within a big data set. In this paper, we identify sentiments expressed in online medical forums that discuss Lyme disease. There are two goals in our research: first, to identify a complete and relevant set of categories that can characterize Lyme disease discourse; and second, to test and investigate strategies, both individually and collectively, for automating the classification of medical forum posts into those categories. We present a feature-based model that consists of three different feature sets: content-free, content-specific and meta-level features. Employing inductive learning algorithms to build a feature-based classification model, we assess the feasibility and accuracy of our automated classification. We further evaluate our model by assessing its ability to adapt to an online medical forum discussing Lupus disease. The experimental results demonstrate the effectiveness of our approach.

An improved framework for authorship identification in online messages

The authorship identification will determine the likelihood of the writing produced, by an author, by means of examining the other writings. The rapid proliferation of technologies along with the applications of the internet, the misuse of online messages for the purpose of inappropriate or for illegal reasons is a major concern in society. The online message distribution and its anonymous nature will make the identity of tracing anyone of critical issue. The work has been developed using a framework for the identification of authorship of the online messages for addressing as well as tracing such problems. For this framework, identification of authorship is done by the four writing style features (the lexical, the syntactic, the structural, and the n-gram features) that are extracted and inductive learning algorithms have been used for building a feature based classification model for the identification of the authorship of the online messages. For this work, the C4.5, the fuzzy and also the Ada boost classifiers will be used for the task of authorship-identification. An experimental study on this framework with the effects of these classification techniques on online messages is evaluated.

Efficient method for updating class association rules in dynamic datasets with record deletion

Association rule mining is an important topic in data mining. The problem is to discover all (or almost all) associations among items in the transaction database that satisfy some user-specified constraints. Usually, the constraints are related to minimal support and minimal confidence. Class association rules (CARs) are a special type of association rules that can be applied for classification problem. Previous research showed that classification based on association rules has higher accuracy than can be achieved with an inductive learning algorithm or C4.5. As such, many methods have been proposed for mining CARs, although these use batch processing. However, datasets are often changed, with records added or/and deleted, and consequently updating CARs is a challenging problem. This paper proposes an efficient method for updating CARs when records are deleted. First, we use an MECR-tree to store nodes for the original dataset. The information in the nodes of this tree are updated based on the deleted records. Second, the concept of pre-large itemsets is used to avoid rescanning the original dataset. Finally, we propose an algorithm to efficiently update and generate CARs. We also analyze the time complexity to show the efficiency of our proposed algorithm. The experimental results show that the proposed method outperforms mining CARs from the dataset after record deletion.

Inductive Supervised Quantum Learning.

In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

A fuzzy rule induction algorithm for discovering classification rules

© 2016 - IOS Press and the authors. All rights reserved. Recently, the topic of data mining has attracted considerable attention from both academia and industry. Data mining is the process of extracting useful knowledge from large amounts of data. Among the types of knowledge to be mined, classification knowledge is the most widely exploited in engineering applications. A variety of methods exist for inductive learning of crisp classification knowledge. This paper presents a new inductive learning algorithm called FuzzyRULES that extracts fuzzy classification rules from a database of examples. The use of fuzzy sets and fuzzy logic methods not only provides a powerful, flexible approach to handling vagueness and uncertainty, but also increases the expressive power and comprehensibility of the induced classification knowledge. An example involving the induction of process planning rules is used to illustrate the operation of FuzzyRULES. The algorithm has also been compared against conventional crisp and fuzzy rule induction algorithms on several benchmark data sets. The results obtained have shown that FuzzyRULES induces more compact and more accurate classification rule sets.