Engineering of Intelligent Systems

Lecture Notes in Computer Science, 2003

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm (SOAP: Selection of Attributes by Projection) has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. The performance of SOAP is analyzed in two ways: percentage of reduction and classification. SOAP has been compared to CFS [4] and ReliefF [6]. The results are generated by C4.5 before and after the application of the algorithms.

Although there have been advancements in the Indian healthcare system over the past few decades, there is still a long way to go before we can claim to have reached world standards. Despite being the second most populated nation, India ranks 143rd out of 195 nations in terms of healthcare infrastructure. Even now, seven decades after India's declaration of independence, the country's healthcare system remains unable to guarantee universal access to care. Access to affordable, high-quality healthcare is still a pipe dream, especially for those who live in rural areas. Not everyone can afford medical services. Private organizations charge a high price for their therapies. No considerable financial assistance is allowed. This study suggests a novel hybrid feature selection method for identifying the most important qualities. Standard feature selection approaches such as Maximum relevance and minimum redundancy (mRMR), Relief, a genetic algorithm, and Least absolute shrinkage and selection operator (LASSO) were compared. A variety of classifiers were used to create a cardiovascular disease prediction system, including logistic regression, Naive Bayes, Random Forest, and support vector machine. This study made use of data from the Cleveland heart disease dataset. According to the results of this research, a Random forest based prediction model trained using characteristics discovered via a new hybrid feature selection may provide the best accuracy and sensitivity. According to the results of the research, applying feature selection algorithms enhances the performance of the prediction system in terms of accuracy, sensitivity, specificity, and throughput.

Machine learning is a field of computer engineering that gives ability to PCs to learn without being unequivocally programmed. Machine learning is used in an arrangement of computational tasks where arranging and programming unequivocal calculations with high performance isn't straightforward. One of the principle goals of machine learning is to get ready PCs to utilize data to deal with a predefined issue. This paper provides an extensive review of studies related to expert estimation of software development using Machine-Learning Techniques (MLT). Machine learning system effectively "learns" how to estimate from training set of completed projects. This paper presents the most commonly used machine learning techniques such as neural networks, case based reasoning, classification and regression trees, rule induction, genetic algorithm & genetic programming for expert estimation in the field of software development.

SSRN Electronic Journal, 2019

Developments in data oriented technology is increasing rapidly. Fact that data is the only asset that is in abundance has enabled a gateway across major developments in field of automation and smartness. Customized models for predicting people's choices as well as decisions have been made much easier. Statistical prediction models based upon available user data are devised. Machine learning is a developing field that provide computers a new level of intelligence thereby evolving new fields of research such as image recognition, character recognition, voice recognition etc. This paper provides a tutorial scheme for all those aiming to brush their intellect on machine intellectual behavior and further deep learning based models. Detailed description of regression and classification algorithms will explain the background statistics that run behind any basic smart prediction model. Different learning models are casted for numerous problems.

Intelligent Data Analysis, 1997

This paper presents an alternative approach to the problem of regression. The methodology we describe allows the use of classification algorithms in regression tasks. From a practical point of view this enables the use of a wide range of existing Machine Learning (ML) systems in regression problems. In effect, most of the widely available systems deal with classification.

International Journal For Research In Applied Science & Engineering Technology, 2020

Machine learning is one of the most exciting technology& it gives the computer that makes it more similar to humans .It is actively being used today, perhaps in many more places than one would expect. Learning is a natural human behaviour which has been made an essential aspect of the machines as well. There are various techniques used for the same. Traditional machine learning algorithms have been applied in many application areas. This paper presents the most commonly used machine learning algorithms such as supervised, unsupervised, reinforcement. In each of our study we found that the results of various machine-learning algorithm depends on application areas on which they are applied. Our review of study not only suggests that these techniques are competitive with traditional estimators on one data set, but also illustrate that these methods are sensitive to the data on which they are trained.

2008

Abstract. Due to the increase in data mining research and applications, selection of interesting rules among a huge number of learned rules is an important task in data mining applications. In this paper, the metrics for the interestingness of a rule is investigated and an algorithm that can classify the learned rules according to their interestingness is developed. Classification algorithms were designed to maximize the number of correctly classified instances, given a set of unseen test cases. Furthermore, feature projection based classification algorithms were tested and shown to be successful in large number of real domains. So, in this work, a feature projection based classification algorithm (VFI, Voting Feature Intervals) is adapted to the rule interestingness problem, and FPRC (Feature Projection Based Rule Classification) algorithm is developed.

The Knowledge Engineering Review, 1999

Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5).

International Journal of Engineering Sciences & Research Technology, 2014

Mining is about to extract the important knowledge over the available dataset. There are number of functionalities available to extract such information over the dataset. One of such approach is classification approach. Dataset Learning is about to categorize the available dataset so that effective knowledge extraction will be done. Learning approaches perform the feature level analysis over the dataset and reduced the processing dataset size so that the performance of the overall mining process will be reduced. In this paper, some of the effective learning approaches are discussed such as Decision Tree, KNN and Bayes Network. The paper has also presented the learning model of disease prediction for agricultural data also

2013

Machine learning [1] is concerned with algorithmically finding patterns and relationships in data, and using these to perform tasks such as classification and prediction in various domains. We now introduce some relevant terminology and provide an overview of a few sorts of machine learning approaches.