This paper proposes Particle Swarm Optimization (PSO) algorithm to discover classification rules. The potential IF-THEN rules are encoded into real-valued particles that contain all types of attributes in data sets. Rule discovery task is formulized into an optimization problem with the objective to get the high accuracy, generalization performance, and comprehensibility, and then PSO algorithm...

Many pattern classification algorithms such as Support Vector Machines (SVMs), MultiLayer Perceptrons (MLPs), and K-Nearest Neighbors (KNNs) require data to consist of purely numerical variables. However many real world data consist of both categorical and numerical variables. In this paper we suggest an effective method of converting the mixed data of categorical and numerical variables into d...

Categorical distributions are ubiquitous in machine learning, e.g., in classification, language models, and recommendation systems. They are also at the core of discrete choice models. However, when the number of possible outcomes is very large, using categorical distributions becomes computationally expensive, as the complexity scales linearly with the number of outcomes. To address this probl...

The present paper thoroughly explores second-best efficient allocations in an adverse selection insurance economy. We start from a natural extension of the classical model, assuming less than perfect risk perceptions. We propose first and second welfare theorems, by means of which we describe efficiencyenhancing policies. Notions of weak and strong adverse selection are promising for interpreti...