Regression on feature projections

Cataloged from PDF version of article.This paper describes a machine learning method, called Regression on Feature Projections (RFP), for predicting a real-valued target feature, given the values of multiple predictive features. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction of the target value for a query point is obtained through two averaging procedures executed sequentially. The ®rst averaging process is to ®nd the individual predictions of features by using the K-Nearest Neighbor (KNN) algorithm. The second averaging process combines the predictions of all features. During the ®rst averaging step, each feature is associated with a weight in order to determine the prediction ability of the feature at the local query point. The weights, found for each local query point, are used in the second prediction step and enforce the method to have an adaptive or context-sensitive nature. We have compared RFP with KNN and the rule based-regression algorithms. Results on real data sets show that RFP achieves better or comparable accuracy and is faster than both KNN and Rule-based regression algorithms. (C)2000 Elsevier Science B.V. All rights reserved

Instance-based regression by partitioning feature projections

Ankara : Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2000.Thesis (Master's) -- Bilkent University, 2000.Includes bibliographical references leaves 87-92.Uysal,İlhanM.S

Concrete utopianism in integrated assessment models: Discovering the philosophy of the shared socioeconomic pathways

The Shared Socioeconomic Pathways (SSPs) are at the forefront of climate change science today. As an influential methodology and method, the SSPs guide the framing of numerous climate change research questions and how these are investigated. Although the SSPs were developed by an interdisciplinary group of scientists in a well-documented process, there is no apparent consensus in the literature that answers the question, "What is the philosophy of science behind the SSPs?" To investigate, the paper applies a systematic thematic qualitative content analysis to the dataset of published papers that establish the rules and expectations for using the SSPs. The research determines that there is no obvious and concise statement on the epistemological and ontological foundation of the SSPs. However, based on the evidence identified in the dataset, SSPs are implicitly, though not explicitly, consistent with a critical realist and concrete utopian philosophy as coined by Roy Bhaskar. This is the first paper to discuss the philosophical underpinning of the SSPs

An Experimental Comparison of Three Machine Learning Techniques for Web Cost Estimation

Many comparative studies on the performance of machine learning (ML) techniques for web cost estimation (WCE) have been reported in the literature. However, not much attention have been given to understanding the conceptual differences and similarities that exist in the application of these ML techniques for WCE, which could provide credible guide for upcoming practitioners and researchers in predicting the cost of new web projects. This paper presents a comparative analysis of three prominent machine learning techniques – Case-Based Reasoning (CBR), Support Vector Regression (SVR) and Artificial Neural Network (ANN) – in terms of performance, applicability, and their conceptual differences and similarities for WCE by using data obtained from a public dataset (www.tukutuku.com). Results from experiments show that SVR and ANN provides more accurate predictions of effort, although SVR require fewer parameters to generate good predictions than ANN. CBR was not as accurate, but its good explanation attribute gives it a higher descriptive value. The study also outlined specific characteristics of the 3 ML techniques that could foster or inhibit their adoption for WCE