Most tree-based algorithms are typical top-down approaches that search only for locally optimal decisions at each node and does not guarantee the globally optimal solution. In this paper, we would like to propose a new evolutionary algorithm for global induction of univariate regression trees and model trees that associate leaves with simple linear regression models. The general structure of ou...

In this paper, an SIR epidemic model with an infectious period and a non-linear Beddington-DeAngelis type incidence rate function is considered. The dynamics of this model depend on the reproduction number R0. Accurately, if R0 < 1, we show the global asymptotic stability of the disease-free equilibrium by analyzing the corresponding characteristic equation and using compa...

An alumina supported Co/Ni catalyst was prepared by sol-gel procedure to study the catalytic behavior during Fischer-Tropsch synthesis in a fixed-bed reactor. The effect of CO conversion (10-50%) on hydrocarbon product distribution (CH4, C5+ and C2-C4 olefin selectivities) was studied. Selectivity for CH4 decreased, while those of C5+<...

This paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. The output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (LLNF) model, is useful for assessing a project status at different time horizons. Being trained by a locally linear model tree (LOLIMOT) learning algorithm, the model is int...

Frick and Grohe [J. ACM 48 (2006), 1184–1206] introduced a notion of graph classes with locally bounded tree-width and established that every first order logic property can be decided in almost linear time in such a graph class. Here, we introduce an analogous notion for matroids (locally bounded branch-width) and show the existence of a fixed parameter algorithm for first order logic propertie...

Creating an interpretable model with high predictive performance is crucial in eXplainable AI (XAI) field. We introduce neural network-based regression for tabular data this study. Our proposed uses ordinary least squares (OLS) as a base-learner, and we re-update the parameters of our base-learner by using networks, which meta-learner model. The updates coefficients confidence interval formula....

A locally connected spanning tree (LCST) T of a graph G is a spanning tree of G such that for each node its neighborhood in T induces a connected subgraph in G. The problem of determining whether a graph contains an LCST or not has been proved to be NP-complete, even if the graph is planar or chordal. The main result of this paper is a linear time algorithm that, given an SC 3-tree (i.e. a maxi...

Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem and present a new algorithm based on a multiresolution search...

Locally weighted polynomial regression (LWPR) is a popular instance-based algorithm for learning continuous non-linear mappings. For more than two or three inputs and for more than a few thousand datapoints the computational expense of predictions is daunting. We discuss drawbacks with previous approaches to dealing with this problem, and present a new algorithm based on a multiresolution searc...