Different Number Of Attributes Research Articles

How Attribute Quantity Influences Option Choice

Products can be described by different numbers of attributes. But can the mere number of attributes presented across a choice-set influence what type of option people choose? This paper demonstrates that attribute numerosity tends to benefit certain types of options more than others and, consequently, has systematic effects on choice. Because attributes often serve as a heuristic cue for product usefulness, they benefit options that are perceived as relatively inferior on this dimension (e.g., hedonics). Consistent with this perspective, five studies demonstrate that attribute numerosity benefits hedonic more than utilitarian options by increasing the extent to which the former are perceived as useful. Consequently, increasing attribute quantity equally across the choice-set shifts choice toward hedonic options, regardless of whether the attributes are hedonic, utilitarian, or mixed in nature. Consistent with this conceptualization, these effects are amplified under heuristic processing and when usefulness is made salient through priming. The findings have important implications for how marketers present attribute information, for public policy and consumer welfare, and for understanding argument numerosity effects in persuasion more broadly.

The Role of Attribute Selection in GIS Representations of the Biophysical Environment

This article questions the arbitrary selection of input attributes for the definition of landform classes and other fiat objects that are used to represent the biophysical environment in geographic information science. It suggests that attribute selection influences the characterization of both geographic and attribute space in these applications. Hence digital elevation model-based fuzzy c-means landform classification relies on sensible selection of terrain attributes to generate fuzzy landform classes (memberships) with biophysical meanings. A case study employed several sets of sensitivity tests and evaluated how selections of terrain attributes may affect the outputs of fuzzy c-means landform classifications. The results showed an average classification difference of 37 percent when different numbers of attributes are used and 18 percent when similar terrain attributes are swapped. Effects of attribute selection also show obvious dependence on spatial resolution and number of classes. These results indicate that the current approach of selecting terrain attributes (not only for landform classifications but also for other applications) according to tacit expert knowledge needs to be better justified. Because the fuzzy c-means classification method is essentially data-driven, the adoption of an exploratory approach as a part of this method is crucial. Such an approach may help to identify membership distributions (and corresponding classifications) that summarize the correspondence between landforms and specific biophysical patterns.

ILA: an inductive learning algorithm for rule extraction

In this paper we present a novel inductive learning algorithm called the Inductive Learning Algorithm (ILA) for extracting production rules from a set of examples. We also describe application of the ILA to a range of data sets with different numbers of attributes and classes. The results obtained show that the ILA is more general and robust than most other algorithms for inductive learning. Most of the time, ILA appears to be comparable to other well-known algorithms, such as AQ and ID3, if not better.

RULES: A simple rule extraction system

We present R ULES, a simple inductive learning algorithm for extracting IF-THEN rules from a set of training examples. We also describe the application of RULES to a range of problems, each involving a different number of attributes, values, and classes. The results obtained demonstrate that in spite of its simplicity RULES is at least comparable to other inductive learning algorithms, if not more accurate and more general.

Measuring preferences with hybrid conjoint analysis: The impact of a different number of attributes in the master design

Experiments, using approximately 300 college seniors, were run to compare the cross-validations of a traditional conjoint model, two self-explicated models, and several hybrid conjoint models. This experiment varied both the number of respondents across which the hybrid task was aggregated and the number of attributes used to describe each concept. The hybrid conjoint model displayed strong cross-validation evidence. Furthermore, the sample sizes, across which the hybrid models were aggregated, had little impact on the predictive power of the hybrid models. Contrary to expectations, increasing the complexity of the design, i.e., the number of attributes in the profiles, did not lead to higher cross-validations for the hybrid models relative to the traditional conjoint model. Consistent with previous studies, segment-level hybrid models did not lead to higher cross-validations than corresponding aggregate models.