Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selectors are algorithms that attempt to identify and remove as much irrelevant and redundant information as possible prior to learning. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the "goodness" of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are pres..

Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure-activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.Peer reviewe

Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a computer system for classifying ECG signals is presented. The MIT-BIH, ECG arrhythmia database is used for analysis. After the ECG signal is noisy in the preprocessing stage, the data feature is extracted. In the feature extraction step, the decision tree is used and the support vector machine (SVM) is constructed to classify the ECG signal into two categories. It is normal or abnormal. The results show that the system classifies the given ECG signal with 90% sensitivity.

This thesis summarizes four of my research projects in machine learning. One of them is on a theoretical challenge of defining and exploring complexity measures for data sets; the others are about new and improved classification algorithms. We first investigate the role of data complexity in the context of binary classification problems. The universal data complexity is defined for a data set as the Kolmogorov complexity of the mapping enforced by that data set. It is closely related to several existing principles used in machine learning such as Occam's razor, the minimum description length, and the Bayesian approach. We demonstrate the application of the data complexity in two learning problems, data decomposition and data pruning. In data decomposition, we illustrate that a data set is best approximated by its principal subsets which are Pareto optimal with respect to the complexity and the set size. In data pruning, we show that outliers usually have high complexity contributions, and propose methods for estimating the complexity contribution. Experiments were carried out with a practical complexity measure on several toy problems. We then propose a family of novel learning algorithms to directly minimize the 0/1 loss for perceptrons. A perceptron is a linear threshold classifier that separates examples with a hyperplane. Unlike most perceptron learning algorithms, which require smooth cost functions, our algorithms directly minimize the 0/1 loss, and usually achieve the lowest training error compared with other algorithms. The algorithms are also computationally efficient. Such advantages make them favorable for both standalone use and ensemble learning, on problems that are not linearly separable. Experiments show that our algorithms work very well with AdaBoost. We also study ensemble methods that aggregate many base hypotheses in order to achieve better performance. AdaBoost is one such method for binary classification problems. The superior out-of-sample performance of AdaBoost has been attributed to the fact that it minimizes a cost function based on the margin, in that it can be viewed as a special case of AnyBoost, an abstract gradient descent algorithm. We provide a more sophisticated abstract boosting algorithm, CGBoost, based on conjugate gradient in function space. When the AdaBoost exponential cost function is optimized, CGBoost generally yields much lower cost and training error but higher test error, which implies that the exponential cost is vulnerable to overfitting. With the optimization power of CGBoost, we can adopt more "regularized" cost functions that have better out-of-sample performance but are difficult to optimize. Our experiments demonstrate that CGBoost generally outperforms AnyBoost in cost reduction. With suitable cost functions, CGBoost can have better out-of-sample performance. A multiclass classification problem can be reduced to a collection of binary problems with the aid of a coding matrix. The quality of the final solution, which is an ensemble of base classifiers learned on the binary problems, is affected by both the performance of the base learner and the error-correcting ability of the coding matrix. A coding matrix with strong error-correcting ability may not be overall optimal if the binary problems are too hard for the base learner. Thus a trade-off between error-correcting and base learning should be sought. In this paper, we propose a new multiclass boosting algorithm that modifies the coding matrix according to the learning ability of the base learner. We show experimentally that our algorithm is very efficient in optimizing the multiclass margin cost, and outperforms existing multiclass algorithms such as AdaBoost.ECC and one-vs-one. The improvement is especially significant when the base learner is not very powerful.</p

Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate better than other existing metrics with human judgements. Other research in this area has presented machine learning methods which learn directly from human judgements. In this paper, we present a novel combination of dependency- and machine learning-based approaches to automatic MT evaluation, and demonstrate greater correlations with human judgement than the existing state-of-the-art methods. In addition, we examine the extent to which our novel method can be generalised across different tasks and domains

Thousands of Machine Learning research papers contain experimental comparisons that usually have been conducted with a single focus of interest, and detailed results are usually lost after publication. Once past experiments are collected in experiment databases they allow for additional and possibly much broader investigation. In this paper, we show how to use such a repository to answer various interesting research questions about learning algorithms and to verify a number of recent studies. Alongside performing elaborate comparisons and rankings of algorithms, we also investigate the effects of algorithm parameters and data properties, and study the learning curves and bias-variance profiles of algorithms to gain deeper insights into their behavior

Proceedings of: 15th International Conference on Machine Learning, Madison (Wisconsin, USA), July 24-27, 1998.Genetic Programming (GP) is a machine learning technique that was not conceived to use domain knowledge for generating new candidate solutions. It has been shown that GP can benefit from domain knowledge obtained by other machine learning methods with more powerful heuristics. However, it is not obvious that a combination of GP and a knowledge intensive machine learning method can work better than the knowledge intensive method alone. In this paper we present a multi-strategy approach where an analytical and inductive approach (hamlet) and an evolutionary technique based on GP (EvoCK) are combined for the task of learning control rules for problem solving in planning. Results show that both methods complement each other, supplying to the other method what the other method lacks and obtaining better results than using each method alone.Publicad

We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the data and to allow a trade-off between accuracy and complexity. The result is a general purpose learning machine, suitable for practical learning tasks, that we call the set covering machine. We present a version of the set covering machine that uses data-dependent balls for its set of features and compare its performance with the support vector machine. By extending a technique pioneered by Littlestone and Warmuth, we bound its generalization error as a function of the amount of data compression it achieves during training. In experiments with real-world learning tasks, the bound is shown to be extremely tight and to provide an effective guide for model selection

This chapter explores the intersection of machine learning (ML) and quantum cryptography, focusing on how next-generation algorithms are enhancing secure computing. It begins with an introduction to quantum cryptography, outlining its fundamental principles and importance in protecting data against classical and quantum attacks. The chapter then delves into the role of machine learning in cryptography, examining its applications in optimizing cryptographic protocols, detecting anomalies, and predicting vulnerabilities. The development of quantum-enhanced algorithms, particularly in Quantum Key Distribution (QKD) and quantum random number generation, is discussed, highlighting how ML is driving innovation in these areas. Practical case studies are presented to demonstrate real-world implementations, followed by an analysis of the challenges and risks associated with deploying ML-enhanced quantum cryptography, including computational complexity, security vulnerabilities, and ethical considerations. The chapter concludes with a discussion on future trends, such as quantum machine learning (QML) and the integration of AI, blockchain, and quantum cryptography, offering a vision for the future of secure computing in the quantum era. Keywords: Quantum Cryptography, Machine Learning, Quantum Key Distribution (QKD), Quantum Random Number Generation, Quantum Computing, Quantum Machine Learning (QML), Post-Quantum Cryptography, Secure Computing, AI in Cryptography, Blockchain Integration, Anomaly Detection, Cryptographic Algorithms, Adversarial Machine Learning, Data Security, Quantum Cryptographic Systems, Next-Generation Algorithms, Cybersecurity, Ethical Considerations in Cryptography, Computational Complexity.

[[abstract]]Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in software engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks and use some software development and maintenance tasks as examples to show how they can be formulated as learning problems and approached in terms of learning algorithms

Machine learning is a branch of artificial intelligence. It enables computers to automatically learn and improve from experience without being explicitly programmed [1]. Machine learning algorithms are usually classified into supervised, unsupervised, neural network-based, and reinforcement learning methods. Supervised learning methods build a mathematical model from a set of data that contains both the inputs and the desired outputs. The model is studied to predict the output values for given inputs [1-2]. These methods can also be used to find patterns (usually called classes) in data and assign new data points to one of the predefined classes. The goal of unsupervised learning is to find meaningful patterns in a set of data points that correspond closely to some intuitive notion of similarity. Neural network-based methods belong to the class of algorithms inspired by biological neural networks [2-4]. Neural networks replace a simulated neuron's simple threshold function with a real-valued differentiable function that gives the neuron the ability to output a real number. Reinforcement learning is an area of machine learning concerned with how an agent should take actions in an environment to maximize a reward signal.

Information presentation in a digital library plays important role especially in improving the usability of collections and helping users to get started with the collection. One approach is to provide an overview through large topical category hierarchies associated with the documents of a collection. But with the growth in the amount of information, this manual classification becomes a new problem for users. The navigation through the hierarchy can be a time-consuming and frustrating process. In this master thesis, we examine the performance of machine learning algorithms for automatic text classification. We examine three learning algorithms namely ID3, Instance Based Learning, and Naive Bayes to classify documents according to their category hierarchies. We focused on the effectiveness measurement such as recall, precision, the F1- measure, error, and the learning curve in learning a manually classified metadata collection from the Indonesian Digital Library Network (IndonesiaDLN), and we compare the results with an examination of the Reuters-21578 dataset. We summarize the algorithm that is most suitable for the digital library collection and the performance of the algorithms on these datasets