Camelia-M. Pintea

Bio: Camelia-M. Pintea is an academic researcher from Technical University of Cluj-Napoca. The author has contributed to research in topics: Ant colony optimization algorithms & Metaheuristic. The author has an hindex of 12, co-authored 52 publications receiving 473 citations. Previous affiliations of Camelia-M. Pintea include Babeș-Bolyai University.

A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop

Abstract: The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par with the performance of dermatologists, yet outperforms the average. As human perception is inherently limited, such approaches can discover patterns, e.g. that two objects are similar, in arbitrarily high-dimensional spaces what no human is able to do. Humans can deal only with limited amounts of data, whilst big data is beneficial for aML; however, in health informatics, we are often confronted with a small number of data sets, where aML suffer of insufficient training samples and many problems are computationally hard. Here, interactive machine learning (iML) may be of help, where a human-in-the-loop contributes to reduce the complexity of NP-hard problems. A further motivation for iML is that standard black-box approaches lack transparency, hence do not foster trust and acceptance of ML among end-users. Rising legal and privacy aspects, e.g. with the new European General Data Protection Regulations, make black-box approaches difficult to use, because they often are not able to explain why a decision has been made. In this paper, we present some experiments to demonstrate the effectiveness of the human-in-the-loop approach, particularly in opening the black-box to a glass-box and thus enabling a human directly to interact with an learning algorithm. We selected the Ant Colony Optimization framework, and applied it on the Traveling Salesman Problem, which is a good example, due to its relevance for health informatics, e.g. for the study of protein folding. From studies of how humans extract so much from so little data, fundamental ML-research also may benefit.

The Generalized Traveling Salesman Problem solved with Ant Algorithms

Abstract: A well known N P-hard problem called the Generalized Traveling Salesman Problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System (ACS) algorithm called Reinforcing Ant Colony System (RACS) which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time.

The generalized traveling salesman problem solved with ant algorithms

Abstract: A well known $$\mathcal{NP}$$ -hard problem called the generalized traveling salesman problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System (ACS) algorithm called reinforcing Ant Colony System which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time.

Emergency management using geographic information systems: application to the first Romanian traveling salesman problem instance

Abstract: The strategic design of logistic networks, such as roads, railways or mobile phone networks, is essential for efficiently managing emergency situations. The geographic coordinate systems could be used to produce new traveling salesman problem (TSP) instances with geographic information systems (GIS) features. The current paper introduces a recurrent framework designed for building a sequence of instances in a systematic way. The framework intends to model real-life random adverse events manifested on large areas, as massive rainfalls or the arrival of a polar front, or targeted relief supply in early stages of the response. As a proof of concept for this framework, we use the first Romanian TSP instance with the main human settlements, in order to derive several sequences of instances. Nowadays state-of-the-art algorithms for TSP are used to solve these instances. A branch-and-cut algorithm delivers the integer exact solutions, using substantial computing resources. An implementation of the Lin---Kernighan heuristic is used to rapidly find very good near-optimal integer solutions to the same instances. The Lin---Kernighan heuristic shows stability on the tested instances. Further work could be done to better exploit GIS features in order to efficiently tackle operations on large areas and to test the solutions delivered by other algorithms on new instances, derived using the introduced framework.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Causability and explainability of artificial intelligence in medicine.

Abstract: Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black-box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use-case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction.