Eclectic rule-extraction from support vector machines

Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule- extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule- extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity

Handling the MAUP: methods for identifying appropriate scales of aggregation based on measures on spatial and non-spatial variance

The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data

Optimizing artificial neural networks using LevyChaotic mapping on Wolf Pack optimization algorithm for detect driving sleepiness

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%

JPEG encoder hardware software partitioning using stochastic hill climbing optimization technique

La partición hardware/software es una etapa clave dentro del proceso de co-diseño de los sistemas embebidos. En esta etapa se decide qué componentes serán implementados como co-procesadores de hardware y qué componentes serán implementados en un procesador de propósito general. La decisión es tomada a partir de la exploración del espacio de diseño, evaluando un conjunto de posibles soluciones para establecer cuál de estas es la que mejor balance logra entre todas las métricas de diseño. Para explorar el espacio de soluciones, la mayoría de las propuestas, utilizan algoritmos metaheurísticos; destacándose los Algoritmos Genéticos, Recocido Simulado. Esta decisión, en muchos casos, no es tomada a partir de análisis comparativos que involucren a varios algoritmos sobre un mismo problema. En este trabajo se presenta la aplicación de los algoritmos: Escalador de Colinas Estocástico y Escalador de Colinas Estocástico con Reinicio, para resolver el problema de la partición hardware/software. Para validar el empleo de estos algoritmos se presenta la aplicación de este algoritmo sobre un caso de estudio, en particular la partición hardware/software de un codificador JPEG. En todos los experimentos es posible apreciar que ambos algoritmos alcanzan soluciones comparables con las obtenidas por los algoritmos utilizados con más frecuencia.Hardware/software partitioning is a key task for embedded system co-design. The goal of this task is to decide which components of an application will be executed in a general purpose processor (software) and which ones on a specific hardware. To support this decision a design space exploration is executed, by the evaluation of several solutions to establish the best trade-off reached. To accomplish this task, metaheuristics algorithms are used by the most proposals; highlighting Genetic Algorithms and Simulated Annealing. Many times this decision is not taken by a comparative study over several algorithms. In this article the application of Stochastic Hill Climbing and Restart Stochastic Hill Climbing for solving the hardware/software partitioning problem is presented. A case study of JPEG encoder is presented. The results show that comparable solutions are reached by those algorithms

RULE-BASED VERSUS STRUCTURE-BASED MODELS FOR EXPLAINING AND GENERATING EXPERT BEHAVIOR

Flexible representations are required in order to understand and generate expert behavior. While production rules with quantifiers can encode experiential knowledge, they often have assumptions implicit in them, making them brittle in problem scenarios where these assumptions do not hold. Qualitative models achieve flexibility by representing the domain entities and their interrelationships explicitly. However, in problem domains where assumptions underlying such models change periodically, it is necessary to be able to synthesize and maintain qualitative models in response to the changing assumptions. In this paper, we argue for a representation that contains partial model components that are synthesized into qualitative models containing entities and relationships relevant to the domain. The model components can be replaced and rearranged in response to changes in the task environment. We have found this "model constructor" to be useful in synthesizing models that explain and generate expert behavior, and have explored its ability to support decision-making in the problem domain of business resource planning, where reasoning is based on models that evolve in response to changing external conditions or internal policies.Information Systems Working Papers Serie

Extended Fuzzy Clustering Algorithms

Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuzzy clustering algorithms. This technical report proposes two extensions to the objective function based fuzzy clustering for dealing with these issues. First, the (point) prototypes are extended to hypervolumes whose size is determined automatically from the data being clustered. These prototypes are shown to be less sensitive to a bias in the distribution of the data. Second, cluster merging by assessing the similarity among the clusters during optimization is introduced. Starting with an over-estimated number of clusters in the data, similar clusters are merged during clustering in order to obtain a suitable partitioning of the data. An adaptive threshold for merging is introduced. The proposed extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithms are given. The properties of the new algorithms are illustrated in various examples

Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)

The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors