New Archive-Based Ant Colony Optimization Algorithms for Learning Predictive Rules from Data

Data mining is the process of extracting knowledge and patterns from data. Classification and Regression are among the major data mining tasks, where the goal is to predict a value of an attribute of interest for each data instance, given the values of a set of predictive attributes. Most classification and regression problems involve continuous, ordinal and categorical attributes. Currently Ant Colony Optimization (ACO) algorithms have focused on directly handling categorical attributes only; continuous attributes are transformed using a discretisation procedure in either a preprocessing stage or dynamically during the rule creation. The use of a discretisation procedure has several limitations: (i) it increases the computational runtime, since several candidates values need to evaluated; (ii) requires access to the entire attribute domain, which in some applications all data is not available; (iii) the values used to create discrete intervals are not optimised in combination with the values of other attributes. This thesis investigates the use of solution archive pheromone model, based on Ant Colony Optimization for mixed-variable (ACOMV) algorithm, to directly cope with all attribute types. Firstly, an archive-based ACO classification algorithm is presented, followed by an automatic design framework to generate new configuration of ACO algorithms. Then, we addressed the challenging problem of mining data streams, presenting a new ACO algorithm in combination with a hybrid pheromone model. Finally, the archive-based approach is extended to cope with regression problems. All algorithms presented are compared against well-known algorithms from the literature using publicly available data sets. Our results have been shown to improve the computational time while maintaining a competitive predictive performance

Archive-Based Pheromone Model for Discovering Regression Rules with Ant Colony Optimization

In this paper we introduce a new algorithm, called Ant-Miner-Reg_MA, to tackle the regression problem using an archive-based pheromone model. Existing regression algorithms handle continuous attribute using a discretisation procedure, either in a preprocessing stage or during rule creation. Using an archive as a pheromone model, inspired by the ACO for Mixed-Variable (ACO_MV), we eliminate the need for a discretisation procedure. We compare the proposed Ant-Miner-Reg_MA against Ant-Miner-Reg, an ACO-based regression algorithm that uses a dynamic discretisation procedure, inspired on M5 algorithm, during rule construction process. Our results show that Ant-Miner-Reg_MA achieved a significant improvement in the relative root mean square error of the models created, overcoming the limitations of the dynamic discretisation procedure

Multithreaded signal-to-memory mapping algorithm for embedded multidimensional signal processing

[abstract not available

Data stream classification with ant colony optimisation

Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACO-based algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures—for example, heuristic calculation, selection of continuous attributes, pruning—require multiple passes through the data to create a model. In this paper, we present a new ACO-based algorithm for data stream classification. The proposed algorithm, called Stream Ant-Miner (sAnt-Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed-type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt-Miner produces statistically significant concise models compared with state-of-the-art rule induction data stream algorithms, without negative effects on their predictive accuracy

Maria Jacobsen, Ruth A. Parmelee, Karen Jeppe : witnessing the Armenian genocide

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2017.This work is a student project of the The Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.by Feyzullahoğlu, Burcu

Instance-based classification with ant colony optimization

Instance-based learning (IBL) methods predict the class label of a new instance based directly on the distance between the new unlabeled instance and each labeled instance in the training set, without constructing a classification model in the training phase. In this paper, we introduce a novel class-based feature weighting technique, in the context of instance-based distance methods, using the Ant Colony Optimization meta-heuristic. We address three different approaches of instance-based classification: k-Nearest Neighbours, distance-based Nearest Neighbours, and Gaussian Kernel Estimator. We present a multi-archive adaptation of the ACO? algorithm and apply it to the optimization of the key parameter in each IBL algorithm and of the class-based feature weights. We also propose an ensemble of classifiers approach that makes use of the archived populations of the ACO? algorithm. We empirically evaluate the performance of our proposed algorithms on 36 benchmark datasets, and compare them with conventional instance-based classification algorithms, using various parameter settings, as well as with a state-of-the-art coevolutionary algorithm for instance selection and feature weighting for Nearest Neighbours classifiers

Addressing regulatory requirements on explanations for automated decisions with provenance – a case study

AI-based automated decisions are increasingly used as part of new services being deployed to the general public. This approach to building services presents significant potential benefits, such as the reduced speed of execution, increased accuracy, lower cost, and ability to adapt to a wide variety of situations. However, equally significant concerns have been raised and are now well documented such as concerns about privacy, fairness, bias and ethics. On the consumer side, more often than not, the users of those services are provided with no or inadequate explanations for decisions that may impact their lives. In this paper, we report the experience of developing a socio-technical approach to constructing explanations for such decisions from their audit trails, or provenance, in an automated manner. The work has been carried out in collaboration with the UK Information Commissioner’s Office (ICO). In particular, we have implemented an automated Loan Decision scenario, instrumented its decision pipeline to record provenance, categorized relevant explanations according to their audience and their regulatory purposes, built an explanation-generation prototype, and deployed the whole system in an online demonstrator

Integrated Particle Swarm and Evolutionary Algorithm Approaches to the Quadratic Assignment Problem

This paper introduces three integrated hybrid approaches that apply a combination of Hierarchical Particle Swarm Optimization (HPSO) and Evolutionary Algorithms (EA) to the Quadratic Assignment Problem (QAP). The approaches maintain a single population. In the first approach, Alternating HPSO-EA (AHE), the population alternates between applying HPSO and EA in successive generations. In the second, more integrated approach, Integrated HPSO-EA (IHE), each population element chooses to apply one of the two algorithms in each generation with some probability. An element applying HPSO in a given generation can be influenced by an element applying EA in that generation, and vice versa. Thus, within the same generation, some elements act as HPSO particles and others as EA population members, and yet the entire population still cooperates. In the third approach, we present a Social Evolutionary Algorithm (SEA), in which the population applies EA, and each population element can choose to apply the PSO-style social mutation operator in each generation with some probability. The three approaches are compared to HPSO and EA using 31 instances of varying size from the QAP instance library

The dual function of explanations: Why computing explanations is of value

The increasing dependence of decision-making on some level of automation has naturally led to discussions about the trustworthiness of such automation, calls for transparent automated decision-making and the emergence of ‘explainable Artificial Intelligence’ (XAI). Although XAI research has produced a number of taxonomies for the explanation of Artificial Intelligence (AI) and Machine Learning (ML) models, the legal debate has so far been mainly focused on whether a ‘right to explanation’ exists in the GDPR. Lately, a growing body of interdisciplinary literature is concentrating on the goals and substance of explanations produced for automated decision-making, with a view to clarify their role and improve their value against unfairness, discrimination and opacity for the purposes of ensuring compliance with Article 22 of the GDPR. At the same time, several researchers have warned that transparency of the algorithmic processes in itself is not enough and tools for better and easier assessment and review of the whole socio-technical system that includes automated decision-making are needed. In this paper, we suggest that generating computed explanations would be useful for most of the obligations set forth by the GDPR and can assist towards a holistic compliance strategy when used as detective controls. Computing explanations to support the detection of data protection breaches facilitates the monitoring and auditing of automated decision-making pipelines. Carefully constructed explanations can empower both the data controller and external recipients such as data subjects and regulators and should be seen as key controls in order to meet accountability and data protection-by-design obligations. To illustrate this claim, this paper presents the work undertaken by the PLEAD project towards ‘explainable-by-design’ socio-technical systems. PLEAD acknowledges the dual function of explanations as internal detective controls (to benefit data controllers) and external detective controls (to benefit data subjects) and leverages provenance-based technology to compute explanations and support the deployment of systematic compliance strategie

The dual function of explanations: Why it is useful to compute explanations

Whilst the legal debate concerning automated decision-making has been focused mainly on whether a ‘right to explanation’ exists in the GDPR, the emergence of ‘explainable Artificial Intelligence’ (XAI) has produced taxonomies for the explanation of Artificial Intelligence (AI) systems. However, various researchers have warned that transparency of the algorithmic processes in itself is not enough. Better and easier tools for the assessment and review of the socio-technical systems that incorporate automated decision-making are needed. The PLEAD project suggests that, aside from fulfilling the obligations set forth by Article 22 of the GDPR, explanations can also assist towards a holistic compliance strategy if used as detective controls. PLEAD aims to show that computable explanations can facilitate monitoring and auditing, and make compliance more systematic. Automated computable explanations can be key controls in fulfilling accountability and data-protection-by-design obligations, able to empower both controllers and data subjects. This opinion piece presents the work undertaken by the PLEAD project towards facilitating the generation of computable explanations. PLEAD leverages provenance-based technology to compute explanations as external detective controls to the benefit of data subjects and as internal detective controls to the benefit of the data controller