A neural tree for classification using convex objective function

Rani, Asha; Foresti, Gian Luca; Micheloni, Christian

doi:10.1016/j.patrec.2015.08.017

Cited by 14 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, many other forms of neural tree formation such as balanced neural tree [50], generalized neural tree [51], and convex objective function neural tree [52], were focused on the tree improvement of neural nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

View full text Add to dashboard Cite

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features.MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ojha

Abraham

Snel

2017

Applied Soft Computing

View full text Add to dashboard Cite

show abstract

“…With the advent of Deep Learning approaches, Convolutional Neural Networks (CNNs) have been introduced in visual recognition tasks yielding to considerable improvements in the performance [17] with respect to more classical solutions [18]. In fact, CNNs are able to extract different features from a given image, representing them as a set of output maps avoiding manual effort in fea-ture engineering.…”

Section: Related Workmentioning

confidence: 99%

Video-Based Convolutional Attention for Person Re-Identification

Zamprogno

Passon

Martinel

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

In this paper we consider the problem of video-based person re-identification, which is the task of associating videos of the same person captured by different and non-overlapping cameras. We propose a Siamese framework in which video frames of the person to re-identify and of the candidate one are processed by two identical networks which produce a similarity score. We introduce an attention mechanisms to capture the relevant information both at frame level (spatial information) and at video level (temporal information given by the importance of a specific frame within the sequence). One of the novelties of our approach is given by a joint concurrent processing of both frame and video levels, providing in such a way a very simple architecture. Despite this fact, out approach achieves better performance than the state-of-the-art on the challenging iLIDS-VID dataset.

show abstract

“…This is very important as using the proper number of neurons in the hidden layers affects the training time and the prediction performance [42] [13]. The model has been applied to solve classification and regression problems [44][47] [38], and various modification of the algorithm can be found in previous literature [40] [43][50] [16][19] [25] [46]. However, most of these methods are non-scalable, and their mathematical formulations are not aptly done.…”

Section: Introductionmentioning

confidence: 99%

Near-Optimal Sparse Neural Trees for Supervised Learning

Chakraborty¹

2021

Preprint

View full text Add to dashboard Cite

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

show abstract

A neural tree for classification using convex objective function

Cited by 14 publications

References 25 publications

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Video-Based Convolutional Attention for Person Re-Identification

Near-Optimal Sparse Neural Trees for Supervised Learning

Contact Info

Product

Resources

About